Abstract
BACKGROUND
Several health behaviors have been associated with hypertension based on clinic blood pressure (BP). Data on the association of health behaviors with nocturnal hypertension and non-dipping systolic BP (SBP) are limited.
METHODS
We analyzed data for participants with ambulatory BP monitoring at the Year 30 Coronary Artery Risk Development in Young Adults (CARDIA) study exam in 2015–2016 (n = 781) and the baseline Jackson Heart Study (JHS) exam in 2000–2004 (n = 1,046). Health behaviors (i.e., body mass index, physical activity, smoking, and alcohol intake) were categorized as good, fair, and poor and assigned scores of 2, 1, and 0, respectively. A composite health behavior score was calculated as their sum and categorized as very good (score range = 6–8), good (5), fair (4), and poor (0–3). Nocturnal hypertension was defined as mean asleep SBP ≥ 120 mm Hg or mean asleep diastolic BP ≥ 70 mm Hg and non-dipping SBP as < 10% awake-to-asleep decline in SBP.
RESULTS
Among CARDIA study and JHS participants, 41.1% and 56.9% had nocturnal hypertension, respectively, and 32.4% and 72.8% had non-dipping SBP, respectively. The multivariable-adjusted prevalence ratios (95% confidence interval) for nocturnal hypertension associated with good, fair, and poor vs. very good health behavior scores were 1.03 (0.82–1.29), 0.98 (0.79–1.22), and 0.96 (0.77–1.20), respectively in CARDIA study and 0.98 (0.87–1.10), 0.96 (0.86–1.09), and 0.86 (0.74–1.00), respectively in JHS. The health behavior score was not associated non-dipping SBP in CARDIA study or JHS after multivariable adjustment.
CONCLUSIONS
A health behavior score was not associated with nocturnal hypertension or non-dipping SBP.
Keywords: ambulatory blood pressure monitoring, blood pressure, health behaviors, hypertension, nocturnal hypertension, non-dipping blood pressure
Blood pressure (BP) is measured in the clinic setting to screen for hypertension, monitor BP control among people taking antihypertensive medication, and assess the risk for future cardiovascular disease (CVD) events. Ambulatory BP monitoring (ABPM) measures BP outside of the clinic setting, typically to confirm the presence of hypertension or monitor BP control.1 BP measurements on ABPM are taken every 15–30 minutes over a 24-hour period including when a person is sleeping.2 In prior studies, more than 40% of all black and 20% of all white adults have had nocturnal hypertension (mean asleep systolic BP [SBP] ≥ 120 mm Hg or mean asleep diastolic BP [DBP] ≥ 70 mm Hg), and a higher asleep BP has been associated with an increased risk for CVD events, independent of clinic and awake BP.3–5 ABPM can also be used to assess the difference in BP between when a person is awake vs. when they are asleep (dipping). In a meta-analysis of 11 prospective studies conducted in Europe, Asia, and South America, over one-quarter of all adults had non-dipping BP (i.e., <10% decline in BP from being awake to asleep).5 Non-dipping BP has been associated with an increased risk for CVD events and all-cause mortality even after adjustment for BP measured in the clinic setting and 24-hour BP on ABPM.6,7
Numerous studies have shown that health behaviors, including body mass index (BMI), physical activity, smoking status, and alcohol intake, are associated with hypertension based on BP measured in the clinic setting.8,9 However, there are few data on the association of health behaviors with BP while asleep and awake-to-asleep BP dipping. The goal of the current analysis was to determine the association of 4 modifiable health behaviors—BMI, physical activity, cigarette smoking, and alcohol intake—with nocturnal hypertension and non-dipping BP. To address this goal, we analyzed data from 2 studies, the Coronary Artery Risk Development in Young Adults (CARDIA) study and the Jackson Heart Study (JHS).
METHODS
Study population
Detailed descriptions of the CARDIA study and JHS protocols have been published.10,11 Briefly, the CARDIA study enrolled a population-based sample of 5,115 whites and blacks, who were 18–30 years old in 1985–1986 at 4 centers in the United States (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA). The JHS is a community-based cohort of 5,306 blacks, aged 21 years and older, recruited in 2000–2004 from the Jackson, Mississippi metropolitan area in the Southeastern United States. The current cross-sectional analysis was restricted to 781 CARDIA study participants who attended the Year 30 exam at the Birmingham, Alabama or Chicago, Illinois field centers in 2015–2016 and 1,046 JHS participants who attended the baseline examination in 2000–2004 who had a complete 24-hour ABPM recording (defined later). The institutional review boards at each participating site approved the study protocols. All participants provided written informed consent.
Data collection
For the CARDIA study, information on race and sex were collected at baseline. The remaining data used in this analysis were obtained at the Year 30 exam for the CARDIA study and the baseline study visit for JHS. A list of covariates and their definitions is available in Supplementary Table 1.
Health behaviors
Four health behaviors were evaluated in the current analysis: BMI, physical activity, smoking status, and alcohol intake (Supplementary Table 2). BMI was calculated by dividing weight in kilograms by height in meters squared. The frequency and duration of moderate and vigorous activities were assessed using the validated CARDIA Physical Activity Questionnaire and JHS Physical Activity Survey.12,13 Standardized questions were used to assess smoking status and time since cessation for former smokers.14 Responses to the alcohol-related questions in each study were used to calculate the number of drinks per week (beer, wine, and/or hard liquor) consumed.
On the basis of groupings used in prior studies, each individual health behavior was categorized as being good, fair, or poor, and scored 2, 1, or 0, respectively (Supplementary Table 3).14,15 A composite health behavior score was created by summing the scores for the 4 health behaviors (range = 0 [least healthy] to 8 [healthiest]). On the basis of the distribution of scores, participants were categorized into very good (score = 6–8), good (score = 5), fair (score = 4), or poor (score = 0–3) levels. This composite health behavior score was analyzed as the primary exposure and the individual health behaviors were analyzed as secondary exposures.
Ambulatory BP monitoring
In the CARDIA study, ABPM was conducted using the Spacelabs 90227 monitor (Snoqualmie, WA) following the Year 30 exam. In the JHS, ABPM was conducted using the Spacelabs 90207 monitor following the baseline examination. SBP and DBP were measured every 30 minutes in the CARDIA study and every 20 minutes in the JHS over a 24-hour period. The times when participants were awake and asleep were determined by actigraphy supplemented by self-report in the CARDIA study and by self-report alone in the JHS. For JHS participants without self-reported sleep information (n = 197), the awake and asleep periods were defined as 10:00 to 20:00 and midnight to 06:00, respectively.16 Mean awake and asleep SBP and DBP were calculated using all available measurements during each respective period. A complete ABPM recording was defined by ≥ 10 awake and ≥ 5 asleep SBP and DBP measurements.16,17 Nocturnal hypertension was defined as a mean asleep SBP ≥ 120 mm Hg or mean asleep DBP ≥ 70 mm Hg. Non-dipping SBP and DBP status, separately, were defined as having <10% decline in BP from being awake to asleep.
Statistical analysis
Characteristics of the participants in each cohort were calculated, overall and by race for the CARDIA cohort. The statistical significance of differences in characteristics between the 2 cohorts was examined using the chi-square test for categorical variables and the t-test for continuous variables. The statistical significance of differences in characteristics by race was also calculated for the CARDIA study participants. The remaining analyses were conducted for participants in the CARDIA and JHS cohorts, separately. The prevalence of nocturnal hypertension and non-dipping SBP and DBP were calculated by levels of the composite health behavior score and each individual health behavior. Poisson regression with robust standard errors was used to calculate adjusted prevalence ratios (PRs) for nocturnal hypertension and non-dipping SBP and DBP, separately, associated with levels of the composite health behavior score (good, fair, and poor vs. very good) and fair and poor vs. good levels of individual health behaviors. An initial model included adjustment for age and sex. A second model included additional adjustment for education, family income, reduced estimated glomerular filtration rate, albuminuria, history of stroke, diabetes, antihypertensive medication use, and mean clinic BP (SBP and DBP for the outcome of nocturnal hypertension and SBP for the outcome of non-dipping SBP and DBP for the outcome of non-dipping DBP). The final model also included adjustment for awake SBP and DBP for the outcome of nocturnal hypertension, and 24-hour SBP or DBP for the outcome of non-dipping SBP or DBP, respectively. For CARDIA study participants, race and field center were also adjusted for in all of the models. The statistical significance of linear trends across levels of the composite health behavior score and individual health behaviors with nocturnal hypertension and non-dipping BP was determined by modeling the exposures as continuous variables with 3 levels of adjustment, as described earlier. Next, among participants in the CARDIA study cohort, differences between whites and blacks in the association of the composite health behavior score with nocturnal hypertension and non-dipping SBP and DBP were assessed by comparing the Wald estimates from models that included and did not include interaction terms of race with levels of the composite health behavior score. An identical approach was used to assess racial differences in the association of individual health behaviors with nocturnal hypertension and non-dipping SBP and DBP. For each cohort, missing data were accounted for by using multiple imputations with chained equations and 10 imputations.18 The fully conditional specification regression and logistic algorithms were used for imputing continuous and categorical variables, respectively. The amount of missing data for each variable is provided in Supplementary Table 4. All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC).
RESULTS
Participant characteristics
Compared with participants in the CARDIA study, JHS participants were older, more likely to be female, black, have an annual family income <$25,000, reduced estimated glomerular filtration rate and were more likely to be taking antihypertensive medication (Table 1). The JHS participants had higher clinic and asleep SBP compared with the CARDIA study participants. The prevalence of nocturnal hypertension and non-dipping SBP and DBP were higher in the JHS. A good level of physical activity was less common whereas good levels of smoking status and alcohol intake were more common among JHS compared with CARDIA study participants. Characteristics of the CARDIA study participants stratified by race are shown in Supplementary Table 5. The distribution of the composite health behavior score is presented in Supplementary Figure 1.
Table 1.
Participant characteristics by study cohort
| Coronary Artery Risk Development in Young Adults study | Jackson Heart Study | P value* | |
|---|---|---|---|
| (n = 781) | (n = 1,046) | ||
| Characteristics | |||
| Age, years | 54.7 (3.7) | 59.2 (10.9) | <0.001 |
| Female, % | 59.7 | 68.1 | <0.001 |
| Blacks, % | 62.1 | 100.0 | <0.001 |
| Education, years | 14.8 (2.6) | 14.9 (4.0) | 0.630 |
| Family Income <$25,000, % | 22.4 | 39.6 | <0.001 |
| Field center—Birminghama, % | 60.2 | — | — |
| Reduced eGFR, % | 4.0 | 6.4 | 0.025 |
| Albuminuriab, % | 9.0 | 11.4 | 0.268 |
| History of stroke, % | 4.3 | 4.0 | 0.745 |
| Diabetes, % | 17.5 | 19.8 | 0.204 |
| Antihypertensive medication use, % | 40.7 | 56.7 | <0.001 |
| SBP, mm Hg | |||
| Clinic | 122 (17) | 127 (17) | <0.001 |
| Awake | 130 (15) | 129 (14) | 0.547 |
| Asleep | 113 (15) | 121 (16) | <0.001 |
| 24-hour | 124 (15) | 126 (14) | 0.003 |
| DBP, mm Hg | |||
| Clinic | 75 (11) | 77 (10) | <0.001 |
| Awake | 81 (9) | 77 (9) | <0.001 |
| Asleep | 67 (9) | 69 (10) | 0.002 |
| 24-hour | 77 (9) | 74 (9) | <0.001 |
| ABPM phenotypes | |||
| Nocturnal hypertension, % | 41.1 | 56.9 | <0.001 |
| Non-dipping SBP, % | 32.4 | 72.8 | <0.001 |
| Non-dipping DBP, % | 20.0 | 41.0 | <0.001 |
| Health behaviors | |||
| Body mass index, % | 0.002 | ||
| Good | 17.4 | 14.2* | |
| Fair | 28.2 | 35.7 | |
| Poor | 54.4 | 50.1 | |
| Physical activity, % | <0.001 | ||
| Good | 32.8 | 20.2* | |
| Fair | 38.1 | 31.3 | |
| Poor | 29.1 | 48.5 | |
| Smoking status, % | 0.001 | ||
| Good | 82.2 | 88.2* | |
| Fair | 2.2 | 1.7 | |
| Poor | 15.6 | 10.2 | |
| Alcohol intake, % | <0.001 | ||
| Good | 52.8 | 61.9* | |
| Fair | 36.3 | 35.8 | |
| Poor | 10.9 | 2.4 |
Numbers in table are mean (SD) or percentage. Reduced eGFR was defined as < 60 ml/min/1.73 m2. Abbreviations: ABPM: ambulatory blood pressure monitoring; DBP: diastolic blood pressure; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure.
aThe data for the Coronary Artery Risk Development in Young Adults (CARDIA) study were collected at Birmingham and Chicago field centers.
bAlbuminuria was defined as an albumin to creatinine ratio ≥30 mg/g.
* P value for comparison of CARDIA study vs. JHS cohorts is <0.05. For variables with more than 2 categories, a global P value was calculated for differences by cohort.
Health behavior score, nocturnal hypertension, and non-dipping BP
The prevalence of nocturnal hypertension was progressively higher at less healthy levels of the composite health behavior score in the CARDIA study (Table 2, top left panel). The prevalence of nocturnal hypertension did not differ across health behavior scores among the JHS participants (Table 2, top right panel). After multivariable adjustment, no association was present between the composite health behavior score and nocturnal hypertension in the CARDIA study or JHS. There were no differences by race in the association between the composite health behavior score and nocturnal hypertension among the CARDIA study participants (P interaction = 0.505).
Table 2.
Association of the composite health behavior score with nocturnal hypertension and non-dipping systolic blood pressure
| Coronary Artery Risk Development in Young Adults study (n = 781) | Jackson Heart Study (n = 1,046) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Levels of composite health behavior score | P trend | Levels of composite health behavior score | P trend | |||||||
| Very good | Good | Fair | Poor | Very good | Good | Fair | Poor | |||
| Health behavior score | 6–8 | 5 | 4 | 0–3 | 6–8 | 5 | 4 | 0–3 | ||
| (n = 255) | (n = 188) | (n = 205) | (n = 133) | (n = 299) | (n = 310) | (n = 282) | (n = 155) | |||
| Nocturnal hypertension | ||||||||||
| Prevalence, % | 34.9 | 39.4 | 40.7 | 56.1 | <0.001 | 56.8 | 57.5 | 54.4 | 60.2 | 0.818 |
| Prevalence ratio (95% confidence interval) | Prevalence ratio (95% confidence interval) | |||||||||
| Model 1 | 1 (Ref) | 1.10 (0.87–1.39) | 1.08 (0.86–1.36) | 1.36 (1.09–1.69) | 0.019 | 1 (Ref) | 1.03 (0.90–1.17) | 1.00(0.87–1.16) | 1.15(0.98–1.34) | 0.208 |
| Model 2 | 1 (Ref) | 1.02 (0.81–1.29) | 0.93 (0.74–1.17) | 1.06 (0.84–1.33) | 0.882 | 1 (Ref) | 0.98 (0.86–1.12) | 0.94(0.81–1.08) | 1.04(0.88–1.22) | 0.946 |
| Model 3 | 1 (Ref) | 1.03 (0.82–1.29) | 0.98 (0.79–1.22) | 0.96 (0.77–1.20) | 0.669 | 1 (Ref) | 0.98 (0.87–1.10) | 0.96(0.86–1.09) | 0.86(0.74–1.00) | 0.059 |
| Non-dipping SBP | ||||||||||
| Prevalence, % | 26.2 | 36.5 | 31.7 | 39.4 | 0.019 | 74.2 | 74.5 | 73.5 | 65.9 | 0.120 |
| Prevalence ratio (95% confidence interval) | Prevalence ratio (95% confidence interval) | |||||||||
| Model 1 | 1 (Ref) | 1.28 (0.97–1.69) | 1.05 (0.79–1.40) | 1.27 (0.94–1.72) | 0.285 | 1 (Ref) | 1.01 (0.92–1.11) | 1.00 (0.91–1.10) | 0.92 (0.80–1.04) | 0.276 |
| Model 2 | 1 (Ref) | 1.28 (0.97–1.68) | 1.01 (0.76–1.34) | 1.16 (0.86–1.56) | 0.673 | 1 (Ref) | 0.99 (0.90–1.09) | 0.98 (0.89–1.09) | 0.89 (0.78–1.02) | 0.131 |
| Model 3 | 1 (Ref) | 1.27 (0.97–1.67) | 1.00 (0.75–1.33) | 1.12 (0.82–1.51) | 0.839 | 1 (Ref) | 0.99 (0.90–1.09) | 0.99 (0.90–1.09) | 0.88 (0.76–1.00) | 0.098 |
| Non-dipping DBP | ||||||||||
| Prevalence, % | 15.7 | 15.9 | 22.9 | 29.5 | 0.001 | 44.0 | 38.0 | 42.6 | 38.4 | 0.450 |
| Prevalence ratio (95% confidence interval) | Prevalence ratio (95% confidence interval) | |||||||||
| Model 1 | 1 (Ref) | 0.96 (0.62–1.48) | 1.29 (0.88–1.90) | 1.61 (1.08–2.40) | 0.012 | 1 (Ref) | 0.88 (0.73–1.06) | 1.01 (0.84–1.21) | 0.96 (0.76–1.22) | 0.959 |
| Model 2 | 1 (Ref) | 0.94 (0.61–1.44) | 1.15 (0.78–1.68) | 1.24 (0.84–1.83) | 0.199 | 1 (Ref) | 0.84 (0.69–1.02) | 0.97 (0.81–1.18) | 0.92 (0.72–1.16) | 0.733 |
| Model 3 | 1 (Ref) | 0.95 (0.62–1.45) | 1.16 (0.80–1.70) | 1.24 (0.84–1.83) | 0.201 | 1 (Ref) | 0.85 (0.70–1.03) | 0.99 (0.82–1.20) | 0.90 (0.70–1.14) | 0.700 |
Model 1: adjusted for age and sex. For the Coronary Artery Risk Development in Young Adults study cohort, additionally adjusted for race and field center. Model 2: adjusting for Model 1 + education, family income < $25,000, reduced eGFR, albuminuria, diabetes, history of stroke, antihypertensive use and clinic SBP. Additionally adjusted for clinic DBP for the outcome of nocturnal hypertension. Model 3: adjusting for Model 2 + Awake SBP and DBP for outcome of nocturnal hypertension and 24-hour SBP or DBP for outcome of non-dipping SBP or DBP, respectively. Abbreviations: DBP, diastolic blood pressure in mm Hg; eGFR, estimated glomerular filtration rate; SBP: systolic blood pressure in mm Hg.
The prevalence of non-dipping SBP and DBP were higher with less healthy levels of the composite health behavior scores in the CARDIA study and no association was present in the JHS (Table 2, middle and bottom panels). Following multivariable adjustment, there was no association between the composite health behavior score and non-dipping SBP or DBP in either study. There was no difference in the association between composite health behavior score and non-dipping SBP or DBP between white and black CARDIA study participants (P values for interaction = 0.117 for non-dipping SBP and 0.439 for non-dipping DBP).
Individual health behaviors, nocturnal hypertension, and non-dipping BP
Among CARDIA study participants, the prevalence of nocturnal hypertension was higher among those with poor vs. good BMI, physical activity, and smoking status (Table 3). Among JHS participants, the prevalence of nocturnal hypertension was higher among those with poor vs. good physical activity and smoking status and was lower in those with poor vs. good alcohol intake. Poor vs. good alcohol intake was associated with a lower PR for nocturnal hypertension among CARDIA study and JHS participants after multivariable adjustment. No statistically significant trends were present between other individual health behaviors and nocturnal hypertension in the CARDIA study or JHS after multivariable adjustment. Among CARDIA study participants, a statistically significant interaction between BMI and race on nocturnal hypertension was present after multivariable adjustment (P interaction = 0.025; Supplementary Table 6). There was no association between BMI and nocturnal hypertension among whites or blacks after multivariable adjustment. No other associations between individual health behaviors and nocturnal hypertension differed by race among the CARDIA study participants (all P values > 0.05; data not shown).
Table 3.
Association of the individual health behaviors with nocturnal hypertension
| Coronary Artery Risk Development in Young Adults study (n = 781) | Jackson Heart Study (n = 1,046) | |||||||
|---|---|---|---|---|---|---|---|---|
| Health behavior category | P trend | Health behavior category | P trend | |||||
| Good | Fair | Poor | Good | Fair | Poor | |||
| Body mass index | ||||||||
| (n = 136) | (n = 220) | (n = 425) | (n = 149) | (n = 373) | (n = 524) | |||
| Prevalence, % | 29.4 | 38.6 | 46.1 | <0.001 | 57.4 | 58.4 | 55.7 | 0.529 |
| Prevalence ratios (95% confidence Interval) | Prevalence ratios (95% confidence Interval) | |||||||
| Model 1 | 1 (Ref) | 1.18 (0.89–1.57) | 1.38 (1.06–1.79) | 0.006 | 1 (Ref) | 0.97 (0.83–1.14) | 1.02 (0.87–1.19) | 0.661 |
| Model 2 | 1 (Ref) | 1.07 (0.81–1.41) | 1.16 (0.90–1.51) | 0.216 | 1 (Ref) | 0.98 (0.84–1.14) | 0.92 (0.79–1.08) | 0.231 |
| Model 3* | 1 (Ref) | 1.15 (0.86–1.53) | 1.24 (0.93–1.66) | 0.124 | 1 (Ref) | 0.97 (0.85–1.11) | 0.98 (0.85–1.12) | 0.823 |
| Physical activity | ||||||||
| (n = 256) | (n = 297) | (n = 228) | (n = 211) | (n = 327) | (n = 508) | |||
| Prevalence, % | 39.8 | 36.6 | 48.5 | 0.067 | 50.8 | 54.1 | 61.2 | 0.007 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 0.89 (0.72–1.09) | 1.17 (0.96–1.43) | 0.124 | 1 (Ref) | 1.11 (0.94–1.30) | 1.18 (1.02–1.36) | 0.023 |
| Model 2 | 1 (Ref) | 0.83 (0.68–1.01) | 1.00 (0.83–1.22) | 0.920 | 1 (Ref) | 1.10 (0.94–1.28) | 1.14 (0.99–1.31) | 0.069 |
| Model 3 | 1 (Ref) | 0.82 (0.68–1.00) | 1.01 (0.84–1.22) | 0.841 | 1 (Ref) | 1.06 (0.92–1.22) | 1.04 (0.91–1.18) | 0.747 |
| Smoking status | ||||||||
| (n = 642) | (n = 17) | (n = 122) | (n = 923) | (n = 17) | (n = 106) | |||
| Prevalence, % | 38.8 | 35.3 | 54.1 | 0.001 | 55.9 | 64.7 | 64.2 | 0.062 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 0.91 (0.53–1.58) | 1.18 (0.97–1.43) | 0.122 | 1 (Ref) | 1.18 (0.85–1.64) | 1.22 (1.05–1.42) | 0.007 |
| Model 2 | 1 (Ref) | 0.67 (0.45–1.01) | 1.05 (0.85–1.29) | 0.779 | 1 (Ref) | 1.18 (0.89–1.58) | 1.17 (1.00–1.37) | 0.039 |
| Model 3 | 1 (Ref) | 0.66 (0.44–0.99) | 0.90 (0.74–1.10) | 0.230 | 1 (Ref) | 1.06 (0.78–1.44) | 0.97 (0.84–1.12) | 0.745 |
| Alcohol intake | ||||||||
| (n = 412) | (n = 283) | (n = 86) | (n = 646) | (n = 376) | (n = 24) | |||
| Prevalence, % | 42.8 | 40.7 | 34.1 | 0.165 | 60.6 | 51.7 | 38.0 | 0.002 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 0.93(0.78–1.11) | 0.80 (0.60–1.07) | 0.124 | 1 (Ref) | 0.89 (0.79–1.00) | 0.65 (0.38–1.10) | 0.013 |
| Model 2 | 1 (Ref) | 0.90 (0.76–1.07) | 0.77 (0.58–1.00) | 0.039 | 1 (Ref) | 0.90 (0.80–1.00) | 0.55 (0.30–1.02) | 0.008 |
| Model 3 | 1 (Ref) | 0.92 (0.78–1.09) | 0.70 (0.53–0.92) | 0.013 | 1 (Ref) | 0.85 (0.77–0.95) | 0.64 (0.40–1.04) | 0.001 |
Model 1: adjusted for age and sex. For the Coronary Artery Risk Development in Young Adults study cohort additionally adjusted for race and field center. Model 2: adjusting for Model 1 + education, family income < $25,000, reduced estimated glomerular filtration rate, albuminuria, diabetes, history of stroke, antihypertensive use and clinic blood pressure. Model 3: adjusting for Model 2 + awake blood pressure.
The prevalence of non-dipping SBP was progressively higher with less healthy BMI in both the CARDIA study and JHS and was progressively lower with less healthy smoking and alcohol intake in the JHS (Table 4). No statistically significant trends were present between the individual health behaviors and non-dipping SBP in the CARDIA study after multivariable adjustment. There were no statistically significant associations between the individual health behaviors and non-dipping DBP (Supplementary Table 7). The multivariable-adjusted association between physical activity and non-dipping SBP differed by race for the CARDIA participants (P interaction = 0.016; Supplementary Table 8). In the race-stratified multivariable-adjusted analysis, the association was not statistically significant among whites or blacks. No other interactions were present between race and individual health behaviors on non-dipping SBP or DBP among the CARDIA study participants after multivariable adjustment (all P values > 0.05; data not shown).
Table 4.
Association of the individual health behaviors with non-dipping systolic blood pressure
| Coronary Artery Risk Development in Young Adults study (n = 781) | Jackson Heart Study (n = 1, 046) | |||||||
|---|---|---|---|---|---|---|---|---|
| Health behavior category | P trend | Health behavior category | P trend | |||||
| Good | Fair | Poor | Good | Fair | Poor | |||
| Body mass index | ||||||||
| (n = 136) | (n = 220) | (n = 425) | (n = 149) | (n = 373) | (n = 524) | |||
| Prevalence, % | 22.1 | 29.5 | 37.2 | 0.001 | 67.0 | 71.7 | 75.4 | 0.041 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 1.24 (0.86–1.79) | 1.39 (0.99–1.96) | 0.049 | 1 (Ref) | 1.06 (0.93–1.20) | 1.13 (1.00–1.28) | 0.020 |
| Model 2 | 1 (Ref) | 1.27 (0.89–1.82) | 1.36 (0.96–1.91) | 0.093 | 1 (Ref) | 1.06 (0.93–1.20) | 1.12 (0.99–1.27) | 0.040 |
| Model 3 | 1 (Ref) | 1.27 (0.89–1.81) | 1.33 (0.95–1.87) | 0.118 | 1 (Ref) | 1.06 (0.93–1.20) | 1.13 (1.00–1.27) | 0.035 |
| Physical activity | ||||||||
| (n = 256) | (n = 297) | (n = 228) | (n = 211) | (n = 327) | (n = 508) | |||
| Prevalence, % | 27.6 | 32.7 | 37.3 | 0.023 | 72.5 | 69.7 | 75.0 | 0.293 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 1.06 (0.82–1.37) | 1.14 (0.87–1.50) | 0.329 | 1 (Ref) | 0.96 (0.86–1.08) | 1.02 (0.92–1.12) | 0.508 |
| Model 2 | 1 (Ref) | 1.06 (0.83–1.37) | 1.06 (0.81–1.39) | 0.693 | 1 (Ref) | 0.95 (0.85–1.06) | 1.00 (0.90–1.11) | 0.741 |
| Model 3 | 1 (Ref) | 1.06 (0.83–1.36) | 1.05 (0.80–1.38) | 0.737 | 1 (Ref) | 0.95 (0.85–1.06) | 0.99 (0.90–1.10) | 0.874 |
| Smoking status | ||||||||
| (n = 642) | (n = 17) | (n = 122) | (n = 923) | (n = 17) | (n = 106) | |||
| Prevalence, % | 32.1 | 35.3 | 33.6 | 0.721 | 74.2 | 64.3 | 62.9 | 0.026 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 1.01 (0.57–1.79) | 0.97 (0.73–1.29) | 0.853 | 1 (Ref) | 0.89 (0.63–1.27) | 0.87 (0.75–1.01) | 0.060 |
| Model 2 | 1 (Ref) | 0.96 (0.53–1.74) | 0.89 (0.67–1.18) | 0.402 | 1 (Ref) | 0.91 (0.64–1.28) | 0.85 (0.73–0.99) | 0.038 |
| Model 3 | 1 (Ref) | 0.94 (0.51–1.71) | 0.85 (0.64–1.13) | 0.270 | 1 (Ref) | 0.89 (0.63–1.26) | 0.84 (0.72–0.97) | 0.019 |
| Alcohol intake | ||||||||
| (n = 412) | (n = 283) | (n = 86) | (n = 649) | (n = 376) | (n = 24) | |||
| Prevalence, % | 37.8 | 25.8 | 28.2 | 0.006 | 77.5 | 66.1 | 54.2 | <0.001 |
| Prevalence ratios (95% confidence interval) | Prevalence ratios (95% confidence interval) | |||||||
| Model 1 | 1 (Ref) | 0.74 (0.59–0.94) | 0.81 (0.57–1.16) | 0.042 | 1 (Ref) | 0.87 (0.80–0.95) | 0.72 (0.50–1.05) | 0.001 |
| Model 2 | 1 (Ref) | 0.78 (0.61–0.98) | 0.84 (0.59–1.20) | 0.082 | 1 (Ref) | 0.88 (0.80–0.96) | 0.72 (0.49–1.04) | 0.001 |
| Model 3 | 1 (Ref) | 0.79 (0.62–0.99) | 0.84 (0.59–1.20) | 0.090 | 1 (Ref) | 0.88 (0.80–0.96) | 0.72 (0.50–1.05) | 0.001 |
Model 1: adjusted for age and sex. For the Coronary Artery Risk Development in Young Adults study cohort, additionally adjusted for race and field center. Model 2: adjusting for Model 1 + education, family income < $25,000, reduced estimated glomerular filtration rate, albuminuria, diabetes, history of stroke, antihypertensive use and clinic SBP. Model 3: adjusting for Model 2 + 24-hour SBP. Abbreviation: SBP, systolic blood pressure in mm Hg.
DISCUSSION
In the current analysis of 2 large cohorts, there were no statistically significant associations present between a composite health behavior score and nocturnal hypertension or non-dipping SBP and DBP after multivariable adjustment. BMI, physical activity, and smoking were not associated with a higher prevalence of nocturnal hypertension after multivariable adjustment. Fair and poor vs. good alcohol intake were associated with lower multivariable-adjusted PRs for nocturnal hypertension in both the CARDIA study and JHS. Individual health behaviors were not associated with non-dipping SBP or DBP after multivariable adjustment.
Both nocturnal hypertension and non-dipping BP have been associated with increased risk for CVD outcomes and mortality.7 Boggia et al. pooled data from 6 population-based cohorts enrolled across 3 continents and reported that a higher SBP at night and high awake-to-asleep SBP ratio were associated with larger hazards for all-cause and CVD mortality, independent of mean awake and 24-hour SBP.7 Given the high prevalence of nocturnal hypertension and non-dipping BP and their associated increased risk for CVD, approaches are needed to lower asleep BP and increase BP dipping.
There is strong evidence from randomized controlled trials (RCTs) that better health behaviors lower clinic BP.19–25 However, only a few population-based studies have examined the association between health behaviors and asleep BP.26–30 In a European study including 3,216 adults referred to a hypertension clinic, 65% of obese patients compared with 45% normal weight patients had non-dipping BP.26 However, the association between BMI and asleep BP was not statistically significant after multivariable adjustment. In a cross-sectional analysis of 1,345 participants from the EffectiVeness of Internet-based DEpressioN Treatment study, physical activity was associated with a larger awake-to-asleep BP decline.27 However, that study did not adjust for mean 24-hour BP. In a cross-sectional study conducted in Brazil, mean asleep SBP and nocturnal dipping were not statistically significantly different between smokers and nonsmokers.28 In a Spanish study with 14 hypertensive men, repeated dinner-time alcohol intake for 7 days was associated with a statistically significantly lower BP after 6 hours of drinking alcohol, compared with the BP measurements obtained in the same individuals after 4 days with no alcohol intake.30 A majority of these studies were conducted among white participants outside of the United States. In contrast, the current analysis included both white and black participants with overrepresentation of blacks, a population with a high prevalence of nocturnal hypertension and non-dipping BP.
Few studies have evaluated the effect of changes in health behaviors on nocturnal hypertension and non-dipping BP. A non-RCT assessing the effects of gastric bypass surgery compared with an intensive lifestyle intervention on obesity-related comorbidities over a period of 1 year demonstrated 85% lower odds for nocturnal hypertension (odd ratio = 0.15; 95% confidence interval = 0.05–0.42) in the surgery compared with lifestyle intervention group.31 In a 3-month follow-up of 572 white men with untreated borderline hypertension in Hypertension Ambulatory Recording Venetia Study, randomization to intensive physical activity vs. usual activity was associated with a statistically significant reduction in asleep SBP.32 No change in asleep SBP was present over a 6-week period among postmenopausal women randomized to immediate smoking cessation vs. their counterparts wait-listed for smoking cessation.33 An intervention study among 42 heavy drinkers showed a reduction in asleep SBP from 119 to 111 mm Hg with 1 month of alcohol abstinence (P value < 0.001).34 However, this study did not have a control group. These studies demonstrate that some behavioral interventions reduce asleep BP. However, these studies included select groups with very poor health behaviors and the generalizability of the results is unclear.
In this study, a higher alcohol intake was associated with a lower prevalence of nocturnal hypertension and non-dipping BP. Alcohol consumption in the evening may have an acute vasodepressor effect.35 A systematic review of crossover trials examining the effects of daily alcohol consumption with dinner reported a net reduction in BP during the first 5 hours after alcohol intake followed by rise in BP.36 These results are consistent with the current findings.
Although better levels of health behavior scores in this study were not associated with a lower prevalence of nocturnal hypertension or non-dipping BP, other approaches have demonstrated promising results. In the dietary approaches to stop hypertension sodium trial, the dietary approaches to stop hypertension diet with sodium reduction vs. a usual diet led to a statistically significantly greater reduction in asleep SBP (–6.5 mm Hg vs. +2.5 mm Hg change among whites and a –8.8 mm Hg vs. 0.2 mm Hg change among blacks).37 Studies have also examined the association between psychosocial factors including stress and social support with non-dipping BP.38 The role of these modifiable factors on the association between health behaviors and nocturnal hypertension and non-dipping BP warrants further investigation.
In the ABPM for Prediction of Cardiovascular Events (MAPEC) study that included 2,156 hypertensive adults with a median follow-up of 5.6 years, taking one or more antihypertensive medications before bedtime was associated with a lower asleep SBP and DBP compared with taking medications in the morning.39 However, in a crossover trial conducted among a subset of participants in the African American Study of Kidney Diseases, there were no statistically significant differences in asleep BP with administration of all once-daily antihypertensive medications at bedtime or in the morning with an additional dose of a new medication at bedtime each compared with the administration of all once-daily antihypertensive medications in the morning.40 Additional RCTs are needed to determine the impact of bedtime dosing of antihypertensive medication on asleep BP and CVD events.
This study has several strengths including large sample sizes from 2 separate cohorts. Conducting identical analyses in 2 different cohorts allowed us to assess the consistency of the associations between health behaviors and nocturnal hypertension and non-dipping BP. In addition, a high proportion of participants were black, a subgroup with a high prevalence of nocturnal hypertension and non-dipping BP. Despite these strengths, the results should be interpreted in the context of certain limitations. Diet history was not collected during the Year 30 CARDIA study exam. For both the CARDIA study and the JHS, only a single 24-hour ABPM was performed. In addition, data on physical activity, smoking, and alcohol intake were self-reported and may have resulted in misclassification, biasing the associations of health behaviors with nocturnal hypertension and non-dipping BP toward the null. ABPM was conducted only in a subset of participants in each cohort, limiting the statistical power for this analysis. Finally, the association between BMI and nocturnal hypertension was different for whites and blacks. This finding should be investigated in a larger study.
In the current analysis of 2 large cohorts, less healthy behaviors were not associated with a higher prevalence of nocturnal hypertension or non-dipping BP after multivariable adjustment. These results highlight the need for future studies to identify factors that may help prevent and reduce the burden of nocturnal hypertension and non-dipping BP.
DISCLOSURE
The author(s) declared no conflict of interest.
Supplementary Material
ACKNOWLEDGMENTS
The authors thank the staffs and participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study and the Jackson Heart Study (JHS). The CARDIA study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201800005I & HHSN268201800007I), Northwestern University (HHSN268201800003I), University of Minnesota (HHSN268201800006I), and Kaiser Foundation Research Institute (HHSN268201800004I). The JHS is supported and conducted in collaboration with Jackson State University (HHSN268201300049C and HHSN268201300050C); University of Mississippi Medical Center (HHSN268201300046C and HHSN268201300047C); and Touglaoo College (HHSN268201300048C) contracts from the NHLBI and the National Center on Minority Health and Health Disparities (NCMHD) at the National Institute of Health (NIH). The current study is also supported by R01 HL117323 from the NHLBI. JNBIII, SJT, DS JES and PM receives research support through the American Heart Association grant SFRN 15SFRN2390002. DS received K24-HL125704 and R01HL137818 from the NHLBI at the NIH.
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