Impact of change over time in self-reported discrimination on blood pressure: implications for inequities in cardiovascular risk for a multi-racial urban community

Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.

Download Full-text

Genetic variance of SGK-1 is associated with blood pressure, blood pressure change over time and strength of the insulin-diastolic blood pressure relationship

Kidney International ◽

10.1111/j.1523-1755.2005.00672.x ◽

2005 ◽

Vol 68 (5) ◽

pp. 2164-2172 ◽

Cited By ~ 45

Author(s):

Fredrik Von Wowern ◽

Garan Berglund ◽

Joyce Carlson ◽

Henrik Mansson ◽

B.O. Hedblad ◽

...

Keyword(s):

Blood Pressure ◽

Diastolic Blood Pressure ◽

Genetic Variance ◽

Pressure Change ◽

Blood Pressure Change ◽

Change Over Time ◽

Over Time

Download Full-text

Genetic variance of SGK-1 is associated with blood pressure, blood pressure change over time and strength of the insulin-diastolic blood pressure relationship

American Journal of Hypertension ◽

10.1016/j.amjhyper.2005.03.238 ◽

2005 ◽

Vol 18 (5) ◽

pp. A85-A85

Author(s):

F VONWOWERN ◽

C FAVA ◽

G BERGLUND ◽

O MELANDER

Keyword(s):

Blood Pressure ◽

Diastolic Blood Pressure ◽

Genetic Variance ◽

Pressure Change ◽

Blood Pressure Change ◽

Change Over Time ◽

Over Time

Download Full-text

Abstract 238: Genetic Risk Score as a Predictor of Incident Hypertension and Blood Pressure Change Over Time

Hypertension ◽

10.1161/hyp.64.suppl_1.238 ◽

2014 ◽

Vol 64 (suppl_1) ◽

Author(s):

Teemu Niiranen ◽

Juhani Mäki ◽

Aki S Havulinna ◽

Veikko Salomaa ◽

Antti Jula

Keyword(s):

Blood Pressure ◽

Genetic Risk ◽

Odds Ratio ◽

Association Studies ◽

Genetic Risk Score ◽

Genome Wide Association Studies ◽

Prevalence Odds Ratio ◽

Change Over Time ◽

Incident Hypertension ◽

Over Time

Little data exist regarding single nucleotide polymorphisms (SNPs) predicting blood pressure (BP) change over time. We genotyped 32 common SNPs in a nationwide cohort aged ≥30 years examined in years 2000 (n=5402) and 2011 (n=3373). We investigated whether genetic risk scores (GRSs) constructed of these variants would predict incident hypertension and BP change over time. We created GRSs for systolic and diastolic BP by multiplying the risk allele count of each SNP by the effect size estimated in published genome-wide association studies. In linear and logistic regression models adjusted for traditional risk factors, 1-unit increases in GRSs were associated with higher systolic and diastolic BP values both at baseline (β±SE, 1.04±0.14 mmHg and 1.11±0.13 mmHg; P<0.0001 for both) and at reinvestigation (β±SE, 0.86±0.18 mmHg and 0.74±0.16 mm Hg; P<0.0001 for both) and with increased hypertension prevalence (odds ratio [95% CI], 1.12 [1.08-1.16] and 1.18 [1.11-1.26]; P<0.0001 for both). Among all participants who were normotensive at baseline and participated in the re-examination (n=2045), the GRSs were not independently associated with BP change over time (β±SE, 0.016±0.18 mmHg and 0.019±0.18 mmHg; P≥0.27 for both). However, in the top tertile of the GRS, the predicted increase in BP compared with the bottom tertile was 1.59±0.78 mmHg greater for systolic BP (P=0.04) and 0.79±0.49 mmHg greater for diastolic BP (P=0.11) and the odds ratio for incident hypertension was 30% higher (P=0.04). Our data show that GRSs are strongly associated with BP and prevalence of hypertension, but only weakly associated with BP increase and incidence of hypertension in a general adult population.

Download Full-text