Validation of the Framingham Hypertension Risk Score in A Middle Eastern Population: Tehran Lipid and Glucose Study (TLGS)

doi:10.21203/rs.3.rs-111198/v1

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Validation of the Framingham Hypertension Risk Score in A Middle Eastern Population: Tehran Lipid and Glucose Study (TLGS)

https://doi.org/10.21203/rs.3.rs-111198/v1

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published 24 Apr, 2021

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Background: The Framingham hypertension risk score is a well-known and simple model for predicting hypertension in adults. In the current study, we aimed to assess the predictive ability of this model in a Middle Eastern population.

Methods: We studied 5,423 participants, aged 20-69 years, without hypertension, who participated in two consecutive examination cycles of the Tehran Lipid and Glucose Study (TLGS). We assessed discrimination based on Harrell’s concordance statistic (c-index) and calibration (graphical comparison of predicted vs. observed). We evaluated the original, recalibrated (for intercept and slope), and revised (for beta coefficients) models.

Results: Over the 3-year follow-up period, 319 participants developed hypertension. The Framingham hypertension risk score performed well in discriminating between individuals who developed hypertension and those who did not (c-index=0.81, 95% CI: 0.79-0.83). Initially, there was a systematic underestimation of the original risk score (events predicted), which was readily corrected by a simple model revision.

Conclusions: The revised Framingham hypertension risk score can be used as a screening tool in public health and clinical practice to facilitate the targeting of preventive interventions in high-risk Middle Eastern people.

Health Economics & Outcomes Research

Health Policy

hypertension

blood pressure

risk prediction

risk score

primary prevention.

Hypertension is a major global health issue due to its high prevalence and importance as a modifiable risk factor for cardiovascular disease and premature mortality all over the world (1). It may be asymptomatic up to the occurrence of clinical complications (2) and is also hard to manage effectively because of the lack of awareness and adherence to the treatment (3, 4).

According to global burden of disease (GBD) 2017, high systolic blood pressure (SBP) is the first leading risk factor for early death and disability, accounting for 10.4 million deaths and 218 million DALYs (5). The number of people with raised blood pressure has increased worldwide, mainly in low- and middle-income countries (6). Factors including population growth, aging, and behavioral risk factors, such as unhealthy diet, tobacco use, lack of physical activity, excess weight, and exposure to persistent stress, are attributable to the growing prevalence of hypertension (7). Hence, one of the global non-communicable disease (NCD) targets, adapted by the World Health Assembly in 2013, is a 25% reduction in the prevalence of high blood pressure, defined as systolic blood pressure≥140 mL/Hg and diastolic blood pressure≥90 mL/Hg, by 2025 (8).

Evidence has shown that the risk of hypertension incidence depends on some clinical factors such as blood pressure, age, and BMI (9-11). Therefore, an individual approach, based on risk stratification and targeted treatment of non-hypertension people who are at high risk for high blood pressure, may be more desirable (12), which requires a simple tool based on the prediction model. To apply such a model, ideally, the model has to be based on demographic and medical variables that are easily plain and available to non-specialized individuals and health care providers (13). Thus, a risk assessment tool would be useful to identify high-risk individuals who should be targeted for early interventions to prevent or postpone the development of hypertension. Such models have potential public health implications and clinical applications in the prevention of hypertension (14).

Accordingly, several models to predict the risk of new-onset hypertension have been developed in different populations (12, 13, 15-20). Framingham hypertension risk score is a well-known and straightforward model for predicting hypertension in adults; it includes only seven simple factors and, with a c-statistic of 0.788, has a good performance in estimating the 4-year risk of developing hypertension among participants in the Framingham study. However, further testing beyond the cohort in which the risk score was developed is necessary before its implementation in a new population (21). We aimed to assess the predictive ability of the Framingham hypertension risk score in a Middle Eastern population-based cohort study.

Study Design and population

The Tehran Lipid and Glucose Study (TLGS) is a population-based prospective study consisting of 15005 participants, aimed to estimate the prevalence and incidence of NCDs (22); the target population is a representative sample of an urban Iranian population, aged 3 to 69 years, living in Tehran, district No.13. The first examination cycle of the study started in 1999–2001 and after that, follow-up examinations have been repeated approximately every 3 years. Detailed description of rationale, design, and methodology of this study have been published (23). Only the participants who attended both the 5^th and 6^th consecutive examination cycles were included. We excluded participants younger than 20 or older than 69 years old (n= 1,181), had prevalent hypertension (n= 2,004), had prevalent cardiovascular disease (n= 278) or serum creatinine values> 2 mg/dL (n= 1), had prevalent diabetes mellitus (n=439), or had missing covariates (n= 1495) at examination cycle 5. After applying the exclusion criteria, 5,423 individuals remained eligible for the current analysis.

Assessment of Hypertension

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured following standardized protocols at each TLGS examination cycle (24). After resting for 15 minutes in sitting position, SBP and DBP were measured twice at the one-minute interval with a standard mercury sphygmomanometer calibrated by the Iranian Institute of Standards and Industrial Researches. The average of the two measures was taken as the systolic and diastolic blood pressures (22, 23).

Hypertension was defined as the SBP ≥ 140 mmHg or DBP ≥ 90 mmHg or the use of antihypertensive medications. We determined the incidence of hypertension by the presence of hypertension at examination cycle 6, among participants free of this condition at examination cycle 5 (Table 1).

Assessment of covariates

Weight was measured in minimal clothes and without shoes on an electronic scale, which was placed on a flat surface and calibrated to zero before measurement. Height was measured in a standing position and without shoes using a tape meter. Body mass index was calculated as “weight (kilograms)/height (meters) squared”. Current smoking, prevalent cardiovascular diseases and parental hypertension were self-reported. Current smokers were defined as a person who smokes cigarettes daily or occasionally. Prevalent cardiovascular diseases were defined as any coronary heart disease (CHD) (myocardial infarction or angiographic proven CHD) and cerebrovascular events (ischemic or hemorrhagic stroke) (22, 23). Diabetes mellitus was defined as a fasting plasma glucose ≥126 mg/dl or use of anti-hyperglycemic agents.

Framingham Hypertension Risk Score prediction model

Framingham hypertension risk score was derived from 1717 individuals (54% women), aged 20 to 69 years old, who were free of hypertension, cardiovascular diseases, and diabetes at the time of the baseline examination of the Framingham Offspring Study in 1979 followed to 2001. A Weibull regression model was computed along with covariates of age, BMI, SBP, and DBP as continuous variables, as well as sex (women vs. men), smoking (current vs. former or never smoker), and parental history of hypertension (both, one, or no parental history) as categorical variables, and an interaction term between age and DBP. The predicted risk of hypertension was calculated for each participant using the below equation:

Where t = time in years between examinations, βi = the regression coefficients of interested covariates and σ = scale parameter. The values of the coefficients and definitions of covariates are in Appendix A.

Statistical Analysis

The 5,423 Participants were followed a single period from examination cycle five to six, contributing to a total of 12,855 person-years at risk. We examined the validity of the Framingham risk score in four stages (21). First, we calculated the Framingham risk score using the -coefficients derived in the Framingham study. Second, we recalibrated the Framingham risk score by updating the intercept; we replaced the intercept and scale parameter of the Framingham risk score with those of the TLGS, considering the linear predictor based on the original model as the offset in the model. Third, we recalibrated the Framingham risk score by another simple updating approach; we updated the intercept and calibration slope, considering the linear predictor as the only covariate in the model. Fourth, we revised the Framingham risk score by a more extensive updating approach (model revision), recalibration, and re-estimation of the coefficient of the sex covariate by fitting a Weibull model, in which the linear predictor and sex are the only covariates. This modeling choice was motivated by a difference between TLGS and Framingham regarding the hazard ratio of sex.

We assessed the performance of the Framingham risk prediction model among the TLGS population according to three evaluations: equality of regression coefficients (hazard ratio, HR); discrimination; and calibration. To compare the coefficients between the TLGS study and the Framingham study, we used a Weibull model using the same covariates in the Framingham model. A Z test statistic was calculated as:

Where βF and βT are the regression coefficients of the Framingham study and the TLGS, respectively, and SE²_F and SE²_T are the squares of the SEs for the two coefficients (25). Next, we assessed discrimination based on Harrell’s concordance statistic (c-index). Calibration included the comparison of the predicted hypertension incidence with the observed incidence for each decile of the risk score in a graphical assessment (calibration plot). The ratio of the predicted to observed risks was also calculated for the whole validation cohort. Furthermore, an additional analysis was conducted by including individuals with diabetes. All of the analyses were done with Stata version 14.2 (StataCorp. 2015. College Station, TX: StataCorp LP.).

Baseline Characteristics

Table 1 shows the baseline characteristics of the 5,423 participants. The mean age of the participants was 38.7 years, and 56.6% were women. Mean systolic and diastolic blood pressure was 108.8 and 73.8 mm Hg, respectively; approximately 26% of participants had a history of hypertension in at least one parent. Between the 5^th and 6^th examination cycles (median, 3.04 years), 319 persons (176 men) developed new-onset hypertension. The incidence rate of hypertension (per 1000 person-years) was 24.8 (95% CI, 22.2 - 27.7).

Comparison between the Framingham and TLGS models

Table 2 shows the coefficients and hazard ratios for the incidence of hypertension in the TLGS in comparison to those of the Framingham Weibull model. The hazard ratio for women versus men was significantly different in the TLGS, compared to that obtained in the Framingham study (0.809 versus 1.260). Besides, the hazard ratio for systolic blood pressure was slightly smaller than that in the Framingham study (1.052 versus 1.070). Still, not all of the other hazard ratios were significantly different between the present study and the Framingham study.

Performance of the Framingham Hypertension Risk Score

Table 3 shows the ratios of predicted to observed hypertension incidence in each decile of the predicted risk. The original Framingham risk score underestimated the observed hypertension risk within each decile; however, recalibration and model revision improved the performance of the model (Figure 1). The ratio of the predicted to observed risks was 0.69 (95% CI, 0.68-0.70)) for the original Framingham risk score and 0.96 (95% CI, 0.95-0.97) for the revised risk score. The C statistic was 0.81 (95% CI: 0.79-0.83) for the original Framingham risk score and 0.82 (95% CI: 0.80-0.84) for the revised risk score.

Including individuals with diabetes

At baseline (examination cycle 5), 316 individuals had diabetes, 52 of whom developed new-onset hypertension during follow-up. Adding these diabetic subjects to the study population resulted in a c-statistic of 0.81 (95% CI: 0.79, 0.84) for the FHS model and 0.82 (95% CI: 0.80, 0.84) for the revised model.

In this population-based cohort study of non-hypertensive adults aged 20 to 69 years, we applied the Framingham hypertension risk function to predict the 3-year absolute risk of incident hypertension. In the TLGS population, HRs of risk factors for incident hypertension events were significantly similar to those obtained in the Framingham study. The only difference of potential importance that we noticed was a different result in the contribution of sex to the risk of hypertension and a slightly lower hazard ratio for systolic blood pressure.

Our study, in contrast to the Framingham study (10), showed that women were less likely to be hypertensive compared to men (HR=0.809). In line with our study findings, some previous studies demonstrated that among individuals with the same age until the sixth decade of life, men have a higher incidence of hypertension compared to women (24, 26-28).

In the current study, we showed that the Framingham hypertension risk score has a high ability to discriminate individuals who developed hypertension and those did not in the TLGS cohort (C statistic=0.82). This risk score systematically underestimated the risk of hypertension; however, it was able to be corrected by the process of recalibration and model revision. We indicated that both recalibrated and revised models have proper calibration for predicting the risk of incident hypertension. The ratio of the predicted to observed risks across the entire score deciles also confirmed the improvement of the revised Framingham risk score.

We did recalibration as the first and the most straightforward step of updating a prediction model in a new population to address systematic over- or under-estimation of the risk (21, 25). We also did model revision since it is a more complicated and extensive approach of updating a prediction model to modify the equation for differences in baseline incidence and the associations between the outcome and risk factors (21). In this way, we addressed the significantly different HR for sex in the TLGS compared to that in the Framingham population. This point affected the performance of the Framingham prediction model in our community slightly.

Given that current ADA guidelines recommend a BP goal of <140/90 mmHg for most patients with diabetes (29), we also assessed the validity of the Framingham risk score by including individuals with diabetes; however, the validity findings provided no marked difference.

Our analysis highlights the need for external validation of a risk prediction model before it is recommended for extensive use in other populations. Without validation, we cannot trust the risk of a specific disease estimated by the model to make the right clinical decision (30). The predictive performance of the Framingham hypertension prediction model has been tested on different populations. Consistent with our findings, the results from the MESA study showed that the Framingham risk score provides good discrimination but underestimates the risk of incident hypertension in some ethnic groups. Still, it could be corrected using a recalibration process (30). Also, the performance of the Framingham risk prediction model was assessed in a younger population (age 18-30 years); the model in the CARDIA population performed well but systematically underestimated the risk (31). In contrast, the 5-year predictive ability of the Framingham risk score in the Whitehall II study was reasonable, given both calibration and discrimination, but slightly overestimated hypertension risk among individuals <50 years old. They showed that reclassification based on the Whitehall model, i.e., the model with the same variables in the Framingham but new beta coefficients in the Whitehall II population, does not improve the prediction (32).

Bozorgmanesh, et al. have developed a point-score system for predicting incident hypertension in the TLGS study (33). The C-statistic for this prediction model was 0.73 among women and 0.74 among men. This is substantially lower than found for the Framingham model in our evaluation. A reason may be that their model did not include the family history of hypertension as a well-known predictor for incident hypertension (12).

It has demonstrated that a targeted preventive strategy in individuals at high risk of developing hypertension is an effective strategy for the prevention of hypertension (34, 35). Platt et al. assessed the effect of the targeted preventive approach through the hypothetical eliminating the risk factor, e.g. quitting smoking from high-risk populations (36). The high-risk primary prevention led to a more considerable reduction in the population SBP distribution and hypertension prevalence than the primary population prevention. This approach had a greater impact on the low-income population (36).

Prediction models for CVDs, e.g. WHO CVD risk scores, are planned to be routinely used in primary health care using data on routinely measured conventional risk factors. Since these data are common to the hypertension prediction model, joining CVD and hypertension risk predictions in primary care can be an opportunity at no extra cost for NCD prevention programs.

Strengths and Limitations

The strength of the current study is that it included a large population-based cohort of both sexes. This study also has several limitations. First, TLGS is comprised of urban adults in Tehran, and generalizing the results to mainly rural individuals should be done with caution. Also, generalization beyond the Middle East may be limited. Second, we defined the incidence of hypertension based on blood pressure measurements taken on a single visit, which may be less accurate than several measurements to confirm hypertension diagnosis; however, it is a common method in observational studies.

Our investigation represents that the revised Framingham hypertension risk score can be used as a screening tool in public health and clinical practice to facilitate targeting of preventive interventions in high-risk Middle Eastern people.

TLGS: Tehran Lipid and Glucose Study

C-index: Harrell’s concordance statistic

GBD: Global Burden of Disease

SBP: Systolic Blood Pressure

DBP: Diastolic Blood Pressure

DALY: Disability-Adjusted Life Year

NCD: Non-Communicable Disease

BMI: Body Mass Index

CHD: Coronary Heart Disease

HR: Hazard Ratio

ADA: The American Diabetes Association

MESA: Multi-Ethnic Study of Atherosclerosis

CARDIA: The Coronary Artery Risk Development in Young Adults Study

Ethics approval and consent to participate

Ethical approval for the TLGS study was obtained from the Ethics Committee of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences. All of the participants provided written informed consent. All methods were carried out in accordance with relevant guidelines and regulations. Approval for undertaking the current project was also obtained from the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (IR.SBMU.ENDOCRINE.REC.1398.055).

Consent for publication

Not applicable.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was funded by grant No. 17845-1 from the Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Authors' contributions

FK and DK raised the presented idea and designed the study. FK and LC conducted the analyses. FK, DK, LC, and EW.S interpreted the results. FK and AA developed the first draft of the manuscript. DK, FA, and EW.S critically reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We express our appreciation to the research team members and to TLGS participants for their contribution to the study.

Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global disparities of hypertension prevalence and control: a systematic analysis of population-based studies from 90 countries. Circulation. 2016;134(6):441-50.
Salem H, Hasan DM, Eameash A, El-Mageed HA, Hasan S, Ali R. Worldwide prevalence of hypertension: A pooled meta-analysis of 1670 studies in 71 countries with 29.5 million participants. Journal of the American College of Cardiology. 2018;71(11 Supplement):A1819.
Chow CK, Teo KK, Rangarajan S, Islam S, Gupta R, Avezum A, et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. Jama. 2013;310(9):959-68.
Pereira M, Lunet N, Azevedo A, Barros H. Differences in prevalence, awareness, treatment and control of hypertension between developing and developed countries. Journal of hypertension. 2009;27(5):963-75.
Gakidou E, Afshin A, Abajobir AA, Abate KH, Abbafati C, Abbas KM, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 2017;390(10100):1345-422.
Zhou B, Bentham J, Di Cesare M, Bixby H, Danaei G, Cowan MJ, et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19· 1 million participants. The Lancet. 2017;389(10064):37-55.
Organization WH. A global brief on hypertension: silent killer, global public health crisis: World Health Day 2013. World Health Organization, 2013.
Organization WH. Global action plan for the prevention and control of noncommunicable diseases 2013-2020: World Health Organization; 2013.
Beck CC, Lopes Ada S, Pitanga FJ. Anthropometric indicators as predictors of high blood pressure in adolescents. Arquivos brasileiros de cardiologia. 2011;96(2):126-33.
Soudarssanane M, Karthigeyan M, Stephen S, Sahai A. Key predictors of high blood pressure and hypertension among adolescents: a simple prescription for prevention. Indian Journal of Community Medicine. 2006;31(3):164.
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. Preventing chronic disease. 2015;12:E184-E.
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. Annals of internal medicine. 2008;148(2):102-10.
Kshirsagar AV, Chiu Yl, Bomback AS, August PA, Viera AJ, Colindres RE, et al. A hypertension risk score for middle‐aged and older adults. The Journal of Clinical Hypertension. 2010;12(10):800-8.
Echouffo-Tcheugui JB, Batty GD, Kivimäki M, Kengne AP. Risk models to predict hypertension: a systematic review. PloS one. 2013;8(7):e67370.
Pearson T, LaCroix A, Mead L, Liang K. The prediction of midlife coronary heart disease and hypertension in young adults: the Johns Hopkins multiple risk equations. American journal of preventive medicine. 1990;6(2 Suppl):23-8.
Fava C, Sjögren M, Montagnana M, Danese E, Almgren P, Engström G, et al. Prediction of blood pressure changes over time and incidence of hypertension by a genetic risk score in Swedes. Hypertension. 2013;61(2):319-26.
Chien K, Hsu H-C, Su T, Chang W, Sung F-C, Chen M-F, et al. Prediction models for the risk of new-onset hypertension in ethnic Chinese in Taiwan. Journal of human hypertension. 2011;25(5):294.
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. Journal of clinical hypertension (Greenwich, Conn). 2013;15(5):344-9.
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. Hypertension Research. 2015;38(6):419.
Kanegae H, Oikawa T, Suzuki K, Okawara Y, Kario K. Developing and validating a new precise risk‐prediction model for new‐onset hypertension: The Jichi Genki hypertension prediction model (JG model). The Journal of Clinical Hypertension. 2018;20(5):880-90.
EW S. Clinical Prediction Models. A Practical Approach to Development, Validation, and Updating. New York, NY: Springer. 2009.
Azizi F, Rahmani M, Emami H, Mirmiran P, Hajipour R, Madjid M, et al. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Sozial-und präventivmedizin. 2002;47(6):408-26.
Azizi F, Madjid M, Rahmani M, Emami H, Mirmiran P, Hadjipour R. Tehran Lipid and Glucose Study (TLGS): rationale and design. Iranian journal of endocrinology and metabolism. 2000;2(2):77-86.
McAlister FA, Straus SE. Measurement of blood presssure: an evidence based review. Bmj. 2001;322(7291):908-11.
D'Agostino RB, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. Jama. 2001;286(2):180-7.
Gillis EE, Sullivan JC. Sex differences in hypertension: recent advances. Hypertension. 2016;68(6):1322-7.
Everett B, Zajacova A. Gender differences in hypertension and hypertension awareness among young adults. Biodemography and social biology. 2015;61(1):1-17.
Mozaffarian D, Benjamin E, Go A, Arnett D, Blaha M, Cushman M, et al. Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38.
Passarella P, Kiseleva TA, Valeeva FV, Gosmanov AR. Hypertension Management in Diabetes: 2018 Update. Diabetes Spectrum. 2018;31(3):218-24.
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(6):1339-45.
Carson AP, Lewis CE, Jacobs Jr DR, 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(6):1015-20.
Kivimäki 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(3):496-501.
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. Journal of hypertension. 2011;29(8):1486-93.
Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. 2018;71(19):e127-e248.
Carey RM, Muntner P, Bosworth HB, Whelton PK. Prevention and Control of Hypertension. <span class="subtitle"><em>JACC</em> Health Promotion Series</span>. 2018;72(11):1278-93.
Platt JM, Keyes KM, Galea S. Efficiency or equity? simulating the impact of high-risk and population intervention strategies for the prevention of disease. SSM-population health. 2017;3:1-8.

Table 1. Baseline Characteristics of the Participants

Characteristic	Baseline Population
No. of participants	5,423
Mean age (SD), y	38.7(11.7)
Women, n (%)	3,067(56.6)
Systolic blood pressure (SD), mm Hg	108.8 (11.8)
Diastolic blood pressure (SD), mm Hg	73.8 (7.9)
Current smoker, n (%)	772 (14.2)
Parental hypertension, n (%)	1399 (25.8)
Mean body mass index (SD), kg/m2	26.9 (4.7)

SD, Standard Deviation.

*Numbers represent mean±SD for continuous variables and percentages are corresponding to “Yes” for dichotomous variables.

Table 2. Hazard Ratios of Risk Factors for Incident Hypertension in the TLGS and Framingham Study

Parameter/Predictor	TLGS Hazard Ratio (95% CI)	Framingham Hazard Ratio (95% CI)	P-value *
Age (per year)	1.22 (1.08 – 1.38 )	1.20 (1.09 – 1.31)	0.72
Women (vs men)	0.81 (0.63 – 1.04)	1.26 (1.09 – 1.46)	0.006
Systolic blood pressure (per 1 mm Hg)	1.05 (1.04 – 1.07)	1.07 (1.06 – 1.08)	0.03
Diastolic blood pressure (per 1 mm Hg)	1.19 (1.10 – 1.29)	1.16 (1.09 – 1.23)	0.74
Current smoking (vs not)	1.35 (0.98 – 1.86)	1.24 (1.06 –1.46)	0.79
Parental hypertension (vs not)	1.43 (1.17 – 1.82)	1.209 (1.05 – 1.40)	0.66
Body mass index (per unit)	1.04 (1.02 – 1.07)	1.039 (1.03 – 1.05)	0.74
Age by diastolic blood pressure	0.99 (0 .99 – 0.99)	0.99 (0.99–0.99)	0.74

*Data show the P-value for the difference in β-Coefficient between this study and the Framingham study (12).

Table 3. Ratios of Predicted to Observed Hypertension Incidence in each Decile of the Risk Predicted by the Framingham Risk Score.

Deciles		1	2	3	4	5	6	7	8	9	10
Original model	Predicted, n*	0.8	2.2	4.1	6.7	10.3	15.4	23.1	35.4	59.3	139.8
	Observed, n‡	0	5.4	4.2	9.3	14.7	24.5	47.8	48.7	75.9	173.1
	Ratio P: O	-	0.41	0.97	0.72	0.72	0.63	0.48	0.73	0.78	.081
Recalibrated for intercept	Predicted, n	0.69	2.1	4.0	6.9	11.1	17.2	26.8	42.6	74.3	182.3
	Observed, n	0	4.2	4.2	9.3	14.7	24.5	47.8	48.7	75.9	173.1
	Ratio P: O	-	0.38	0.96	0.75	0.76	0.70	056	0.87	0.98	1.05
Recalibrated for intercept & slop	Predicted, n	1.3	3.5	6.2	9.9	14.8	21.6	31.5	46.7	75.0	163.0
	Observed, n	0	5.4	4.2	9.3	14.7	24.5	47.8	48.7	75.9	173.1
	Ratio P: O	-	0.65	1.5	1.07	1.01	0.88	0.66	0.96	0.99	0.94
Revised for the coefficient of sex	Predicted, n	1.1	3.1	5.8	9.2	14.0	21.0	31.3	47.2	76.3	165.4
	Observed, n	0	4.2	4.1	9.0	11.9	30.9	39.2	52.5	79.2	172.7
	Ratio P: O	-	0.73	1.4	1.02	1.2	0.68	0.80	0.90	0.96	0.96

n, number; P, predicted numbers; O, observed numbers.

*The average number of predicted hypertension cases in each decile.

‡The average number of observed hypertension cases in each decile.

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