Cardiovascular Health Trajectories and Elevated C‐Reactive Protein: The CARDIA Study
The relationship between long‐term cardiovascular health (CVH) patterns and elevated CRP (C‐reactive protein) in late middle age has yet to be investigated. We aimed to assess this relationship.
Methods and Results
Individual CVH components were measured in 4405 Black and White men and women (aged 18–30 years at baseline) in the CARDIA (Coronary Artery Risk Development in Young Adults) study at 8 examinations over 25 years. CRP was measured at 4 examinations (years 7, 15, 20, and 25). Latent class modeling was used to identify individuals with similar trajectories in CVH from young adulthood to middle age. Multivariable Poisson regression models were used to assess the association between race‐specific CVH trajectories and prevalence of elevated CRP levels (>3.0 mg/L) after 25 years of follow‐up. Five distinct CVH trajectories were identified for each race. Lower and decreasing trajectories had higher prevalence of elevated CRP relative to the highest trajectory. Prevalence ratios for elevated CRP in lowest trajectory groups at year 25 were 2.58 (95% CI, 1.89–3.51) and 7.20 (95% CI, 5.09–10.18) among Black and White people, respectively. Prevalence ratios for chronically elevated CRP (elevated CRP at 3 or more of the examinations) in the lowest trajectory groups were 8.37 (95% CI, 4.37–16.00) and 15.89 (95% CI, 9.01–28.02) among Black and White people, respectively.
Lower and decreasing CVH trajectories are associated with higher prevalence of elevated CRP during the transition from young adulthood to middle age.
coronary artery risk development in young adults
What Is New?
Latent class modeling was used to identify race‐specific trajectories of ideal cardiovascular health scores over 25 years of follow‐up.
Relative risks of elevated C‐reactive protein, either at 1 or multiple examinations, were higher for lower cardiovascular health trajectories and trajectories that declined over time.
What Are the Clinical Implications?
The study findings suggest that achieving and maintaining a favorable ideal cardiovascular health score may be beneficial for the chronic inflammatory burden associated with cardiovascular disease.
Cardiovascular disease (CVD) is the leading cause of death in the United States.1 In 2010, the American Heart Association established “Life’s Simple 7,” a conglomeration of metrics aimed at defining cardiovascular health (CVH).2 Seven health factors (blood cholesterol, blood pressure [BP], and fasting plasma glucose) and behaviors (diet quality, physical activity [PA], smoking, and body mass index [BMI]) are emphasized within the construct, each being scored either into ideal, intermediate, or poor categories. The sum of the scores for each factor provides an individual’s CVH score. Artero et al. found that the achievement of each additional ideal CVH metric was associated with a graded reduction in risk of cardiovascular disease (CVD) mortality.3 Furthermore, multiple studies have found inverse associations of CVH score and future CVD outcomes.4, 5, 6, 7, 8, 9
Cardiovascular disease has been classified as a chronic inflammatory disease.10 CRP (C‐reactive protein) is a systemic inflammatory marker, secreted primarily by hepatocytes in response to interleukin‐6 and tumor necrosis factor‐alpha.11 CRP activates multiple atherogenic processes including, but not limited to, monocyte cytokine expression, expression of adhesion molecules, and platelet aggregation.11 Multiple studies support CRP as an independent risk factor for CVD, with its individual predictive utility comparable to that of total and high‐density lipoprotein cholesterol or BP.12, 13, 14, 15 The American Heart Association and Centers for Disease Control and Prevention classify an elevated CRP concentration of >3.0 mg/L as high risk.16 Although CRP is a significant, independent predictor of CVD outcomes, a recent review found that when added to traditional risk factor models, the performance of CRP in improving CVD risk classification was inconsistent across 25 studies,17 with minimal effect on prediction shown in the largest study (increased C‐index by 0.0039 and net reclassification improvement of 1.52%).18 Nevertheless, the American Heart Association and American College of Cardiology recommend the additional measurement of newer risk markers, such as CRP, to inform treatment decisions if quantitative risk assessment via traditional risk factors results in an uncertain treatment decision.19 While we recognize these previous studies and their findings on the associations between CRP and CVD, we propose to explore a different relationship where traditional CVD risk factors, compiled into the CVH score, are associated with the risk of elevated CRP levels.
Although individual components of CVH have been associated with inflammatory markers,20, 21, 22, 23, 24, 25, 26, 27 earlier work suggests that the predictive value of the CVH score exceeds that of any of its individual parts.28 Cross‐sectional studies have found an inverse relationship between CVH and CRP,29, 30, 31 but no studies have examined whether different long‐term patterns in CVH are associated with elevated CRP in late middle age. Therefore, the aims of our study were to (1) determine whether CVH trajectory throughout adulthood was associated with prevalent elevated CRP in late‐middle age, and (2) determine whether CVH trajectory throughout adulthood was associated with chronically elevated CRP in Black and White adults.
The data used in this analysis are available through the CARDIA (Coronary Artery Risk Development in Young Adults) study (https://www.cardia.dopm.uab.edu/) upon request.
The CARDIA study is a multicenter prospective cohort of 5115 Black and White men and women examining the determinants of clinical and subclinical CVD and its risk factors. Participants, aged 18 to 30 years at baseline (year 0), were recruited from 4 regions within the United States: Birmingham, AL; Chicago, IL; Minneapolis, MN, and Oakland, CA. Enrollment in the study was balanced at each site by sex, age (18–24 vs. 25–30 years), race, and education. Follow‐up of participants was performed at years 2, 5, 7, 10, 15, 20, and 25 with retention rates of 90%, 86%, 81%, 77%, 74%, 72%, and 72%, respectively. Further details on design and recruitment for the study have been published.32 Institutional review board approval was obtained annually by each field center, and all participants provided written informed consent at each examination. Standardized protocols were used at each center across all examinations. Participants were asked to fast for at least 12 hours before each examination and avoid smoking or heavy PA at least 2 hours prior.
Determination of CVH Score
CVH status was determined at each examination between years 0 through 25 according to American Heart Association criteria (Table S1).2 A 14‐point CVH score was determined by summing points for each CVH metric at ideal (2 points), intermediate (1 point), and poor (0 points) levels, with a final score of 0 corresponding to meeting poor criteria for all 7 components and 14 corresponding to meeting ideal criteria for all components (ie, a higher CVH score is more desirable).
Smoking status was attained via self‐report. Thresholds for CVH smoking score are the following: 0, Poor=current smoker; 1, Intermediate=former smoker, quit within last 12 months; and 2, Ideal=Never smoker or quit >1 year ago.
Body Mass Index
Height and weight were measured at each examination while participants wore light examination clothing and no shoes. Height was measured to the nearest 0.5 cm via vertical ruler and weight to the nearest 0.2 kg with a calibrated balance beam scale. BMI was then calculated as weight in kg divided by height in meters squared. Thresholds for CVH BMI score are as follows: 0, Poor=≥30 kg/m2; 1, Intermediate=≥25 and ≤30 kg/m2; and 2, Ideal=<25 kg/m2.
PA was measured using the CARDIA Physical Activity History questionnaire, which inquires about time spent per week among 13 categories of PA over the past 12 months.33 Physical activity level was expressed as exercise units (EU) of total activity involving moderate to vigorous intensity PA. Thresholds for CVH PA score were as follows: 0, Poor=<100 exercise units; 1, Intermediate=≥100; and <300 exercise units; and 2, Ideal=≥300 exercise units.
A trained interviewer administered a diet history questionnaire, developed specifically for the CARDIA study, at years 0, 7, and 20.34, 35 The CVH diet score is based upon 5 recommended measures, which include (1) ≥4.5 cups/d of fruits/vegetables, (2) ≥2 servings (3.5 oz.) of fish per week (3.5‐oz servings), (3) <1500 mg/d of sodium, (4) <450 kcal (36 oz.)/wk of sweets/sugar‐sweetened beverages, and (5) ≥3 servings/d of whole grains. Thresholds for the CVH diet score are 0, Poor=achievement of 0‐1 diet recommendation; 1, Intermediate=achievement of 2‐3 diet recommendations; and 2, Ideal=achievement of 4‐5 diet recommendations. Diet data from year 0 was carried forward to determine diet status for years 2 and 5, while year 7 diet data carried forward for years 10 and 15. Year 20 diet data were also carried forward to determine diet status for year 25.
Fasted blood draws were taken according to standard protocol.32 Total cholesterol (TC) was measured via enzymatic assay. Threshold for CVH TC score were 0, Poor=TC ≥240 mg/dL; 1, Intermediate=TC ≥200 mg/dL and <240 or being treated for hypercholesterolemia; and 2, Ideal=TC <200 mg/dL.
BP was measured after 5 minutes of rest on the right arm by trained technicians using a random zero sphygmomanometer in the first 6 examinations (years 0 through 15). Years 20 and 25 BP was measured using an Omron digital BP monitor. All pressures were measured in triplicate with the average of the final 2 measurements used for analysis. Thresholds for CVH BP score are 0, Poor=systolic blood pressure ≥140 or diastolic blood pressure ≥90 mm Hg; 1, Intermediate= systolic blood pressure ≥120 and <140 mm Hg or diastolic blood pressure ≥80 and <90 mm Hg or use of hypertensive medications; and 2, Ideal=untreated systolic blood pressure <120 and diastolic blood pressure <80.
Fasting blood glucose was measured using the hexokinase method in years 0, 2, and 5, and via hexokinase coupled to glucose‐6‐phosphate dehydrogenase in years 7, 10, 15, 20, and 25. Thresholds for CVH blood glucose score are 0, Poor=fasting blood glucose ≥126 mg/dL; 1, Intermediate=fasting blood glucose ≥100 and <126 mg/dL or use of diabetic medications; and 2, Ideal=fasting blood glucose <100 mg/dL.
Fasting plasma samples from years 7, 15, 20, and 25 were used to measure CRP via a Roche latex‐particle enhanced immunoturbidimetric assay kit. Assays were read on the Roche Modular P Chemistry analyzer. The assay range for CRP was 0.175 to 1100 μg/mL.36 Elevated CRP was classified as a concentration >3.0 mg/L. Chronically elevated CRP was classified as elevated CRP levels on 3 or more of the 4 available examinations (years 7, 15, 20, and 25).
Latent class modeling was used to identify groups that share a similar underlying trajectory in CVH score over the first 8 examinations. Of the 5115 participants, 710 did not have CVH measured on at least 3 examinations and thus were excluded from the analyses. Race‐specific trajectories were modeled among all 4405 CARDIA participants with CVH measured at 3 or more examinations. Race stratification of trajectories was used to consider disparities in CRP levels and the risk factors that make up the CVH score. This model was fit using SAS Proc Traj using the censored normal model.37 Bayesian information criterion as well as the number of participants in each trajectory (>5% of total race‐specific population) were used to assess model fit. Trajectory group characteristics were compared via ANOVA, Kruskal–Wallis, or χ2 tests as appropriate. Multivariable Poisson regression was used to model the predictive value of CVH trajectory group on both the probability of having elevated CRP at year 25, and the probability of chronically elevated CRP from years 7 to 25. All models were adjusted for sex, center, current age, and current education level.
Sensitivity analysis was performed using CVH score from only years 0, 7, and 20 to test for the effect of diet data carry‐forward. Race‐specific trajectory groups were modeled among the same 4405 participants included in the original analysis via SAS Proc Traj.37 Identical Poisson regression models were also used to model the predictive value of 3‐examination CVH trajectory group on both the probability of having elevated CRP at year 25 and the probability of chronically elevated CRP from years 7 to 25. Further sensitivity analysis was done utilizing the original trajectories while removing individuals with measured CRP ≥10 mg/L at any examination from the regression models. All analyses were performed using SAS 9.4 (Cary, NC).
CVH trajectories were modeled among 4405 (2136 Black participants, 2269 White participants) CARDIA participants with CVH measurements at 3 or more examinations. Of these, 3336 (1523 Black participants, 1813 White participants) had CRP measurements at year 25 and 2549 (1050 Black participants, 1499 White participants) had CRP measurements at all 4 examinations used to classify chronically elevated CRP.
CVH Trajectory Groups
Five distinct trajectories in CVH from young adulthood to middle age were identified for each race (Figure 1): 10.3% (n=220) of Black participants and 6.8% (n=155) of White participants started at a low CVH and progressively decreased (low decreasing group); 18.3% (n=391) of Black participants and 10.0% (n=227) of White participants maintained a low‐moderate CVH level throughout follow‐up (low‐moderate stable group); 31.8% (n=680) of Black participants and 18.7% (n=424) of White participants started at a low‐moderate CVH level and decreased (low‐moderate decreasing group); 24.2% (n=517) of Black participants and 32.8% (n=744) of White participants started at a high‐moderate CVH level and decreased (high‐moderate decreasing); and 15.4% (n=328) of Black participants and 31.7% of White participants (n=719) maintained high CVH levels throughout (high stable group).
In general, each trajectory group displayed steady decreases in CVH score over time; however, the high stable group remained relatively the same and the low‐moderate stable group showed an initial decline and then maintenance of CVH for the remainder of the examinations (Tables S2 and S3). In Black participants, the high‐moderate decreasing group experienced the largest decline in CVH (3.1, SE 0.1) while in White participants, the low‐moderate decreasing group declined the most (3.1, SE 0.1).
The high‐stable group for both races completed more years of education than the other trajectory groups. Graded worsening of individual CVH components was generally seen when moving from higher to lower trajectory groups as well (Tables 1 and 2).
|Low‐Decreasing||Low‐Moderate Decreasing||Low‐Moderate Stable||High‐Moderate Decreasing||High Stable||P Value|
|Age, mean (SE), y||24.0 (0.3)||24.5 (0.1)||24.7 (0.2)||24.3 (0.2)||24.4 (0.2)||0.19|
|Study center, n (%)|
|Birmingham, AL||62 (28.2)||191 (28.1)||93 (23.8)||138 (26.7)||64 (19.5)||0.01|
|Chicago, IL||49 (22.3)||134 (19.7)||96 (24.6)||99 (19.1)||72 (22.0)|
|Minneapolis, MN||57 (25.9)||143 (21.0)||87 (22.3)||109 (21.1)||62 (18.9)|
|Oakland, CA||52 (23.6)||212 (31.2)||115 (29.4)||171 (33.1)||130 (39.6)|
|Sex, n (%)|
|Male||86 (39.1)||259 (38.1)||178 (45.5)||225 (43.5)||154 (47.0)||0.03|
|Female||134 (60.9)||421 (61.9)||213 (54.5)||292 (56.5)||174 (53.0)|
|Education, n (%)|
|Less than high school||58 (26.4)||103 (15.1)||47 (12.0)||40 (7.7)||12 (3.7)||<0.0001|
|High school||85 (38.6)||271 (39.9)||132 (33.8)||191 (36.9)||95 (29.0)|
|Postsecondary||67 (29.1)||236 (34.7)||159 (40.7)||203 (39.3)||140 (427)|
|Bachelor's degree||9 (4.1)||56 (8.2)||38 (9.7)||63 (12.2)||66 (20.1)|
|Graduate or professional||4 (1.8)||14 (2.1)||15 (3.8)||20 (3.9)||15 (4.6)|
|CVH score, mean (SE)||7.2 (0.1)||9.0 (0.1)||9.4 (0.1)||10.8 (0.0)||11.5 (0.1)||<0.0001|
|CVH components, mean (SE) unless labeled otherwise|
|Smoking status, n (%)*|
|Never smoker||58 (26.4)||327 (48.1)||208 (53.2)||401 (77.6)||266 (81.1)||<0.0001|
|Former smoker||13 (5.9)||61 (9.0)||29 (7.4)||45(8.7)||41 (12.5)|
|Current smoker||145 (65.9)||286 (42.1)||153 (39.1)||70 (13.5)||20 (6.1)|
|Body mass index, kg/m2||30.6 (0.5)||26.9 (0.2)||25.2 (0.3)||23.5 (0.2)||22.0 (0.1)||<0.0001|
|Physical activity, exercise units||271.2 (16.4)||347.2 (11.3)||360.1 (15.1)||413.3 (12.9)||495.2 (18.0)||<0.0001|
|Healthy diet score, number of components||0.8 (0.05)||0.9 (0.0)||0.8 (0.0)||1.1 (0.0)||1.4 (0.1)||<0.0001|
|Total cholesterol, mg/dL||198.5 (2.6)||180.1 (1.4)||178.3 (1.8)||172.8 (1.3)||170.0 (1.6)||<0.0001|
|Systolic blood pressure, mm Hg||117.4 (0.8)||113.0 (0.4)||110.7 (0.5)||109.5 (0.4)||108.5 (0.6)||<0.0001|
|Diastolic blood pressure, mm Hg||72.3 (0.8)||69.7 (0.4)||68.0 (0.5)||67.8 (0.4)||67.3 (0.5)||<0.0001|
|Fasting serum glucose, mg/dL||91.2 (3.1)||82.5 (0.5)||80.6 (0.4)||79.8 (0.4)||78.9 (0.4)||<0.0001|
|Low‐Decreasing||Low‐Moderate Decreasing||Low‐Moderate Stable||High‐Moderate Decreasing||High Stable||P Value|
|Age, mean (SE), y||25.1 (0.3)||25.4 (0.2)||24.5 (0.2)||25.7 (0.1)||25.7 (0.1)||<0.0001|
|Study center, n (%)|
|Birmingham, AL||48 (31.0)||112 (26.4)||66 (29.1)||160 (21.5)||89 (12.4)||<0.0001|
|Chicago, IL||26 (16.8)||74 (17.5)||40 (17.6)||175 (23.5)||193 (26.8)|
|Minneapolis, MN||57 (36.8)||151 (35.6)||85 (37.4)||236 (31.7)||208 (28.9)|
|Oakland, CA||24 (15.5)||87 (20.5)||36 (15.9)||173 (23.3)||229 (31.9)|
|Sex, n (%)|
|Male||81 (52.3)||253 (59.7)||114 (50.2)||383 (51.5)||242 (33.7)||<0.0001|
|Female||74 (47.7)||171 (40.3)||113 (49.8)||361 (48.5)||477 (66.3)|
|Education, n (%)|
|Less than high school||27 (17.4)||37 (8.7)||23 (10.1)||30 (4.0)||7 (1.0)||<0.0001|
|High school||57 (36.8)||111 (26.2)||73 (32.2)||150 (20.2)||76 (10.6)|
|Postsecondary||47 (30.3)||155 (36.6)||71 (31.3)||236 (31.7)||170 (23.6)|
|Bachelor's degree||16 (10.3)||75 (17.7)||40 (17.6)||198 (26.6)||292 (40.6)|
|Graduate or professional||8 (5.2)||46 (10.8)||20 (8.8)||130 (17.5)||174 (24.2)|
|CVH score, mean (SE)||7.5 (0.1)||9.8 (0.1)||9.0 (0.1)||11.0 (0.0)||12.1 (0.0)||<0.0001|
|CVH components, mean (SE) unless labeled otherwise|
|Smoking status, n (%)*|
|Never smoker||38 (24.5)||190 (44.8)||55 (24.2)||464 (62.4)||526 (73.2)||<0.0001|
|Former smoker||10 (6.5)||68 (16.0)||33 (14.5)||142 (19.1)||152 (21.1)|
|Current smoker||104 (67.1)||163 (38.4)||136 (59.9)||137 (18.4)||37 (5.1)|
|Body mass index, kg/m2||29.3 (0.5)||25.3 (0.2)||25.0 (0.3)||23.1 (0.1)||21.6 (0.1)||<0.0001|
|Physical activity, exercise units||294.7 (18.7)||422.0 (13.2)||383.3 (16.2)||45.9 (10.4)||525.6 (10.6)||<0.0001|
|Healthy diet score, number of components||0.9 (0.1)||1.2 (0.1)||0.9 (0.1)||1.6 (0.0)||2.1 (0.0)||<0.0001|
|Total cholesterol, mg/dL||195.8 (3.1)||182.0 (1.6)||185.2 (2.5)||175.3 (1.2)||167.0 (1.0)||<0.0001|
|Systolic blood pressure, mm Hg||116.1 (1.0)||111.9 (0.1)||110.4 (0.7)||109.6 (0.4)||105.6 (0.3)||<0.0001|
|Diastolic blood pressure, mm Hg||73.1 (0.9)||69.3 (0.5)||67.9 (0.7)||68.8 (0.3)||66.6 (0.3)||<0.0001|
|Fasting serum glucose, mg/dL||89.9 (2.3)||83.7 (0.4)||83.1 (0.7)||82.5 (0.4)||81.6 (0.3)||<0.0001|
Year 25 Elevated CRP
The overall prevalence of elevated CRP at year 25 was 38.9% in Black participants and 19.5% in White participants, which significantly differed between trajectory groups (P<0.0001 for trend in both Black and White participants). The prevalence of elevated CRP was higher in Black participants for all trajectory groups relative to White participants , varying from 14.3% in the high‐stable group to 34.1% in the low decreasing group for Black participants and 7.1% to 32.3%, respectively, in White participants (Table 3). Between the 2 groups that started at a similar CVH level (low‐moderate stable and low‐moderate decreasing), the low‐moderate decreasing group had a higher prevalence of elevated CRP at year 25 for both races (23.3% vs. 37.4%, respectively, for Black participants, 16.3% vs. 24.5% for White participants).
|No. (%) of Participants With CRP >3.0 mg/L at Y25||Relative Risk (95% CI)*||No. (%) of Participants With CRP >3.0 mg/L at Y25||Relative Risk (95% CI)*|
|CVH Trajectory Group||Black Participants||White Participants|
|High stable||47 (14.3)||1 (reference)||51 (7.1)||1 (reference)|
|High‐moderate decreasing||126 (24.3)||1.8 (1.4, 2.5)||111 (14.9)||2.5 (1.8, 3.4)|
|Low‐moderate stable||91 (23.3)||1.6 (1.2, 2.2)||37 (16.3)||3.0 (2.0, 4.4)|
|Low‐moderate decreasing||254 (37.4)||2.7 (2.0, 3.5)||104 (24.5)||5.0 (3.6, 6.8)|
|Low decreasing||75 (34.1)||2.6 (1.9, 3.5)||50 (32.3)||7.2 (5.1, 10.2)|
Among both Black and White participants, all lower CVH trajectories were at an increased risk relative to the high stable group. Among Black participants, adjusted relative risks for elevated CRP at year 25 ranged from 1.6 (95% CI, 1.2–2.2) for low‐moderate stable, to 2.7 (95% CI, 2.0–3.5) for the low‐moderate decreasing group as compared with high stable. Adjusted relative risks for elevated CRP at Year 25 for White participants ranged from 2.5 (95% CI, 1.8–3.4) for high‐moderate decreasing, to 7.2 (95% CI, 5.1–10.2) for the low decreasing group compared with high stable (Table 3).
Chronically Elevated CRP
The prevalence of chronically elevated CRP across years 7 to 25 was 27.3% in Black participants and 11.3% in White participants in the total sample. This prevalence ranged from 3.0% in the high stable group to 20.0% in the low‐decreasing group among Black participants and 2.2% to 22.6%, respectively, in White participants (P<0.0001 for trend in both Black and White participants, Table 4). Among both low‐moderate groups (low‐moderate stable and low‐moderate decreasing), the low‐moderate decreasing group had higher prevalence of chronically elevated CRP for both races (11.0% vs. 18.7%, respectively, for Black participants, 5.7% vs. 14.2% for White participants).
|No. (%) of Participants With Chronically Elevated CRP||Relative Risk (95% CI)*||No. (%) of Participants With Chronically Elevated CRP||Relative Risk (95% CI)*|
|CVH Trajectory Group||Black Participants||White Participants|
|High stable||10 (3.0)||1 (reference)||16 (2.2)||1 (reference)|
|High‐moderate decreasing||63 (12.2)||4.5 (2.4–8.5)||45 (6.0)||3.5 (2.0–6.1)|
|Low‐moderate stable||43 (11.0)||3.9 (2.0–7.4)||13 (5.7)||3.8 (1.8–7.8)|
|Low‐moderate decreasing||127 (18.7)||7.0 (3.8–13.0)||60 (14.2)||10.3 (6.1–17.6)|
|Low decreasing||44 (20.0)||8.4 (4.4–16.0)||35 (22.6)||15.9 (9.0, 28.0)|
Among Black participants, adjusted relative risks for chronically elevated CRP ranged from 3.9 (95% CI, 2.0–7.4) for low‐moderate stable, to 8.4 (95% CI, 4.4–16.0) for the low decreasing group compared with high stable. Among White participants, adjusted relative risks for chronically elevated CRP ranged from 3.5 (95% CI, 2.0–6.1) for high‐moderate decreasing, to 15.9 (95% CI, 9.0–28.0) for the low decreasing group compared with high stable (Table 4).
Sensitivity analysis with CVH score taken at only 3 examinations resulted in similar trajectory patterns (Figure S1) and trends in prevalence ratios, with less favorable CVH trajectories associated with greater prevalence of elevated and chronically elevated CRP (Tables S4 and S5). Further analysis utilizing the original trajectories but removing individuals with CRP ≥10 mg/L at any examination also revealed similar results (Tables S6 and S7).
We found 5 unique race‐specific trajectories in CVH score from young adulthood to late middle age. Lower and decreasing trajectories in CVH score were independently associated with elevated CRP in late middle age, as well as chronically elevated CRP from years 7 to 25 of the CARDIA study. Associations persisted after adjustment for baseline or year 25 CVH score. In support of the importance of achieving and/or maintaining favorable CVH throughout adulthood, we found the low‐moderate stable group had a greater prevalence of chronically elevated CRP versus the high‐moderate decreasing group, although both groups had a similar CVH score at year 25. When comparing the 2 outcomes of interest (elevated CRP at year 25 and chronically elevated CRP), we found that the prevalence ratios for chronically elevated CRP were considerably larger than those for elevated CRP at year 25, regardless of trajectory group.
The results of our study provide novel insights into long‐term patterns of CVH, with the identification of heterogeneous subgroups of individuals with similar CVH scores at each examination. Our findings highlight that long‐term trajectories in CVH may be associated with CRP levels in late middle age. Our findings also emphasize the importance of achieving and maintaining high CVH throughout adulthood in order to reduce the odds of having elevated CRP. In a meta‐analysis of 54 prospective studies encompassing 1.3‐million‐person years at risk, CRP concentrations were positively correlated with systolic BP as well as non‐high‐density lipoprotein cholesterol and BMI.14 We observed the same general trend in our investigation: all groups experienced a decrease in CVH components and total CVH score over the 25‐year period, which was correlated with increased CRP levels.
Ishii et al. found that in the CARDIA cohort, increased BMI and female sex were associated with increased odds of 1‐time and repeated elevations in CRP (>10 mg/L).38 Notably, 69.9% of repeated CRP elevations were in obese women. This relationship may have influenced our results because there were significant sex and BMI differences between trajectories; however, the joint effect of PA and diet on BMI also should not be ignored in considering influential CVH factors when it comes to elevated CRP levels. Having PA, diet, and BMI, among the other CVH factors in our analysis, may offer a more comprehensive look at how these factors interact with CRP levels. Furthermore, CVH score was found to be an independent predictor of elevated CRP levels when adjusting for sex in our models.
Previous studies have also examined CVH trajectories and their associations with CVD‐related outcomes.39, 40, 41 Of note, each of these studies also identified 5 distinct trajectories for CVH. In a pooled cohort analysis of >9000 individuals, the highest (ie, most favorable) CVH trajectory group had significantly lower carotid intima‐media thickness versus other groups.40 Analyses from the Kailuan study also found that when comparing with the lowest trajectory groups, higher level trajectory groups displayed lower arterial stiffness (as measured by brachial–ankle pulse wave velocity) as well as a lower incidence of CVD.39, 41 Thus, these findings, along with our current study, corroborate the relationship of CVH trajectories with clinical and subclinical CVD outcomes as well as other biomarkers of CVD risk. This collective literature on CVH trajectories throughout the lifespan further highlights the importance of promoting, achieving, and maintaining optimal CVH.
Some strengths and limitations of our study should be noted. The calculated CVH scores took into account medication use for BP, cholesterol levels, and glucose, increasing the real‐world applicability of the results. Our study population was a large, well‐characterized cohort of Black and White Americans followed up over 25 years. The results of our study may not be generalizable to other race/ethnic groups. Although race‐specific CVH trajectories were created, we did not also separate trajectories by sex. We categorized participants as having elevated CRP based on a single measurement at each examination. Although CRP is an acute‐phase protein and circulating levels of CRP are subject to variability, a meta‐analysis of 54 studies found that the year‐to‐year stability of circulating CRP concentration among the same individuals was generally similar to that for systolic BP and TC.14 CRP measurements were available at 4 time points—years 7, 15, 20, and 25—but we do not have information on CRP levels before these time points. The diet score component of CVH was measured at years 0, 7, and 20, with the score from previous years being factored into overall CVH score until the next true measurement (ie, diet score from year 0 was carried over into years 2 and 5). However, our sensitivity analysis with only truly measured diet score (3 examinations) produced similar trajectories and associations with elevated CRP levels.
Our study found that adulthood trajectories of CVH are independently associated with elevated levels of CRP. Our results emphasize the importance of achieving and maintaining favorable CVH from young adulthood to middle age.
Sources of Funding
The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by contracts HHSN268201800003I, HHSN268201800004I, HHSN268201800005I, HHSN268201800006I, and HHSN268201800007I from the National Heart, Lung, and Blood Institute (NHLBI). AD Lane‐Cordova receives funding from the American Heart Association, 18CDA34110038. MA Sarzynski is supported by NIH grants R01HL146462, R01NR019628, and P20GM103499.
This manuscript has been reviewed by CARDIA for scientific content.
Drs Ruiz‐Ramie and Sarzynski had full access to the data in this study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Ruiz‐Ramie and Sarzynski are responsible for the conception and study design. Drs Ruiz‐Ramie, Lane‐Cordova, and Sarzynski conducted statistical analysis. Drs Lane‐Cordova and Sarzynski supervised the analysis. Dr Ruiz‐Ramie drafted the manuscript. All authors were responsible for critical revision of the manuscript.
Supplementary Material for this article is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.120.019725
For Sources of Funding and Disclosures, see page 9.
- 1 Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, et al. Heart disease and stroke statistics‐2019 update: a report from the American heart association. Circulation. 2019; 139:e56–e528. DOI: 10.1161/CIR.0000000000000659LinkGoogle Scholar
- 2 Lloyd‐Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation. 2010; 121:586–613. DOI: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar
- 3 Artero EG, Espana‐Romero V, Lee DC, Sui X, Church TS, Lavie CJ, Blair SN. Ideal cardiovascular health and mortality: Aerobics Center Longitudinal Study. Mayo Clin Proc. 2012; 87:944–952. DOI: 10.1016/j.mayocp.2012.07.015.CrossrefMedlineGoogle Scholar
- 4 Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, Sacco RL. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and hispanics: the northern Manhattan study. Circulation. 2012; 125:2975–2984. DOI: 10.1161/CIRCULATIONAHA.111.081083.LinkGoogle Scholar
- 5 Fang J, Yang Q, Hong Y, Loustalot F. Status of cardiovascular health among adult Americans in the 50 States and the District of Columbia, 2009. J Am Heart Assoc. 2012; 1:
e005371. DOI: 10.1161/JAHA.112.005371.LinkGoogle Scholar
- 6 Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD. Aric Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol. 2011; 57:1690–1696. DOI: 10.1016/j.jacc.2010.11.041.CrossrefMedlineGoogle Scholar
- 7 Ford ES, Greenlund KJ, Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States. Circulation. 2012; 125:987–995. DOI: 10.1161/CIRCULATIONAHA.111.049122.LinkGoogle Scholar
- 8 Kulshreshtha A, Vaccarino V, Judd SE, Howard VJ, McClellan WM, Muntner P, Hong Y, Safford MM, Goyal A, Cushman M. Life's Simple 7 and risk of incident stroke: the reasons for geographic and racial differences in stroke study. Stroke. 2013; 44:1909–1914. DOI: 10.1161/STROKEAHA.111.000352.LinkGoogle Scholar
- 9 Lachman S, Peters RJ, Lentjes MA, Mulligan AA, Luben RN, Wareham NJ, Khaw KT, Boekholdt SM. Ideal cardiovascular health and risk of cardiovascular events in the EPIC‐Norfolk prospective population study. Eur J Prev Cardiol. 2016; 23:986–994. DOI: 10.1177/2047487315602015.CrossrefMedlineGoogle Scholar
- 10 Ross R. Atherosclerosis–an inflammatory disease. N Engl J Med. 1999; 340:115–126. DOI: 10.1056/NEJM199901143400207.CrossrefMedlineGoogle Scholar
- 11 Prasad K. C‐Reactive protein and cardiovascular diseases. Int J of Angiol. 2003; 12:1–12. DOI: 10.1007/s00547‐003‐1018‐y.CrossrefGoogle Scholar
- 12 Ridker PM, Hennekens CH, Buring JE, Rifai N. C‐reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000; 342:836–843. DOI: 10.1056/NEJM200003233421202.CrossrefMedlineGoogle Scholar
- 13 Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH. Plasma concentration of C‐reactive protein and risk of developing peripheral vascular disease. Circulation. 1998; 97:425–428. DOI: 10.1161/01.CIR.97.5.425.LinkGoogle Scholar
- 14 Collaboration ERF, Kaptoge S, Di Angelantonio E, Lowe G, Pepys MB, Thompson SG, Collins R, Danesh J. C‐reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta‐analysis. Lancet. 2010; 375:132–140. DOI: 10.1016/S0140‐6736(09)61717‐7CrossrefMedlineGoogle Scholar
- 15 Kaptoge S, Seshasai SRK, Gao P, Freitag DF, Butterworth AS, Borglykke A, Di Angelantonio E, Gudnason V, Rumley A, Lowe GDO, et al. Inflammatory cytokines and risk of coronary heart disease: new prospective study and updated meta‐analysis. Eur Heart J. 2014; 35:578–589. DOI: 10.1093/eurheartj/eht367.CrossrefMedlineGoogle Scholar
- 16 Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO, Criqui M, Fadl YY, Fortmann SP, Hong Y, Myers GL, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: A statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003; 107:499–511. DOI: 10.1161/01.CIR.0000052939.59093.45.LinkGoogle Scholar
- 17 Lin JS, Evans CV, Johnson E, Redmond N, Coppola EL, Smith N. Nontraditional risk factors in cardiovascular disease risk assessment: updated evidence report and systematic review for the US preventive services task force. JAMA. 2018; 320:281–297. DOI: 10.1001/jama.2018.4242.CrossrefMedlineGoogle Scholar
- 18 Collaboration ERF, Kaptoge S, Di Angelantonio E, Pennells L, Wood AM, White IR, Gao P, Walker M, Thompson A, Sarwar N, et al. C‐reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012; 367:1310–1320. DOI: 10.1056/NEJMoa1107477CrossrefMedlineGoogle Scholar
- 19 Goff DC, Lloyd‐Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O’Donnell CJ, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014; 129:S49–S73. DOI: 10.1161/01.cir.0000437741.48606.98.LinkGoogle Scholar
- 20 Shiels MS, Katki HA, Freedman ND, Purdue MP, Wentzensen N, Trabert B, Kitahara CM, Furr M, Li Y, Kemp TJ, et al. Cigarette smoking and variations in systemic immune and inflammation markers. J Natl Cancer Inst. 2014; 106: DOI: dju294. DOI: 10.1093/jnci/dju294.CrossrefMedlineGoogle Scholar
- 21 Choi J, Joseph L, Pilote L. Obesity and C‐reactive protein in various populations: a systematic review and meta‐analysis. Obes Rev. 2013; 14:232–244. DOI: 10.1111/obr.12003.CrossrefMedlineGoogle Scholar
- 22 Palmefors H, DuttaRoy S, Rundqvist B, Borjesson M. The effect of physical activity or exercise on key biomarkers in atherosclerosis–a systematic review. Atherosclerosis. 2014; 235:150–161. DOI: 10.1016/j.atherosclerosis.2014.04.026.CrossrefMedlineGoogle Scholar
- 23 Neale EP, Batterham MJ, Tapsell LC. Consumption of a healthy dietary pattern results in significant reductions in C‐reactive protein levels in adults: a meta‐analysis. Nutr Res. 2016; 36:391–401. DOI: 10.1016/j.nutres.2016.02.009.CrossrefMedlineGoogle Scholar
- 24 Arena R, Arrowood JA, Fei DY, Helm S, Kraft KA. The relationship between C‐reactive protein and other cardiovascular risk factors in men and women. J Cardiopulm Rehabil. 2006; 26(5):323–327. DOI: 10.1097/00008483‐200609000‐00009.CrossrefMedlineGoogle Scholar
- 25 Chuang SY, Hsu PF, Chang HY, Bai CH, Yeh WT, Pan HW. C‐reactive protein predicts systolic blood pressure and pulse pressure but not diastolic blood pressure: the Cardiovascular Disease Risk Factors Two‐Township Study. Am J Hypertens. 2013; 26:657–664. DOI: 10.1093/ajh/hps095.CrossrefMedlineGoogle Scholar
- 26 Lee CC, Adler AI, Sandhu MS, Sharp SJ, Forouhi NG, Erqou S, Luben R, Bingham S, Khaw KT, Wareham NJ. Association of C‐reactive protein with type 2 diabetes: prospective analysis and meta‐analysis. Diabetologia. 2009; 52:1040–1047. DOI: 10.1007/s00125‐009‐1338‐3.CrossrefMedlineGoogle Scholar
- 27 Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C‐reactive protein levels in overweight and obese adults. JAMA. 1999; 282:2131–2135. DOI: 10.1001/jama.282.22.2131.CrossrefMedlineGoogle Scholar
- 28 Xanthakis V, Enserro DM, Murabito JM, Polak JF, Wollert KC, Januzzi JL, Wang TJ, Tofler G, Vasan RS. Ideal cardiovascular health: associations with biomarkers and subclinical disease and impact on incidence of cardiovascular disease in the Framingham Offspring Study. Circulation. 2014; 130:1676–1683. DOI: 10.1161/CIRCULATIONAHA.114.009273.LinkGoogle Scholar
- 29 Xue H, Wang J, Hou J, Gao J, Chen S, Zhu H, Wang Y, Chen Y, Wu S. Ideal cardiovascular health behaviors and factors and high sensitivity C‐reactive protein: the Kailuan cross‐sectional study in Chinese. Clin Chem Lab Med. 2014; 52:1379–1386. DOI: 10.1515/cclm‐2013‐0657.CrossrefMedlineGoogle Scholar
- 30 Xue H, Wang J, Hou J, Zhu H, Gao J, Chen S, Wang Y, Chen Y, Wu S. Association of ideal cardiovascular metrics and serum high‐sensitivity C‐reactive protein in hypertensive population. PLoS One. 2013; 8:
e81597. DOI: 10.1371/journal.pone.0081597.CrossrefMedlineGoogle Scholar
- 31 González‐Gil EM, Santabárbara J, Ruiz JR, Bel‐Serrat S, Huybrechts I, Pedrero‐Chamizo R, de la O A, Gottrand F, Kafatos A, Widhalm K, et al. Ideal cardiovascular health and inflammation in European adolescents: The HELENA study. Nutr Metab Cardiovasc Dis. 2017; 27:447–455. DOI: 10.1016/j.numecd.2016.12.003.CrossrefMedlineGoogle Scholar
- 32 Friedman GD, Cutter GR, Donahue RP, Hughes GH, Hulley SB, Jacobs DR, Liu K, Savage PJ. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988; 41:1105–1116. DOI: 10.1016/0895‐4356(88)90080‐7.CrossrefMedlineGoogle Scholar
- 33 Jacobs DR, Hahn LP, Haskell WL, Pirie P, Sidney S. Validity and reliability of short physical activity history: Cardia and the Minnesota heart health program. J Cardiopulm Rehabil. 1989; 9:448–459. DOI: 10.1097/00008483‐198911000‐00003.CrossrefMedlineGoogle Scholar
- 34 McDonald A, Van Horn L, Slattery M, Hilner J, Bragg C, Caan B, Jacobs D, Liu K, Hubert H, Gernhofer N, et al. The CARDIA dietary history: development, implementation, and evaluation. J Am Diet Assoc. 1991; 91:1104–1112.CrossrefMedlineGoogle Scholar
- 35 Liu K, Slattery M, Jacobs D, Cutter G, McDonald A, Van Horn L, Hilner JE, Caan B, Bragg C, Dyer A, et al. A study of the reliability and comparative validity of the cardia dietary history. Ethn Dis. 1994; 4:15–27.MedlineGoogle Scholar
- 36 Ebong IA, Schreiner P, Lewis CE, Appiah D, Ghelani A, Wellons M. The association between high‐sensitivity C‐reactive protein and hypertension in women of the CARDIA study. Menopause. 2016; 23:662–668. DOI: 10.1097/GME.0000000000000609.CrossrefMedlineGoogle Scholar
- 37 Nagin DS, Odgers CL. Group‐based trajectory modeling (nearly) two decades later. J Quant Criminol. 2010; 26:445–453. DOI: 10.1007/s10940‐010‐9113‐7.CrossrefMedlineGoogle Scholar
- 38 Ishii S, Karlamangla AS, Bote M, Irwin MR, Jacobs DR, Cho HJ, Seeman TE. Gender, obesity and repeated elevation of C‐reactive protein: data from the CARDIA cohort. PLoS One. 2012; 7:
e36062. DOI: 10.1371/journal.pone.0036062.CrossrefMedlineGoogle Scholar
- 39 Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris‐Etherton PM, Wu Y, Jin C, Huang S, Hu FB, et al. Association of trajectory of cardiovascular health score and incident cardiovascular disease. JAMA Netw Open. 2019; 2:
e194758. DOI: 10.1001/jamanetworkopen.2019.4758.CrossrefMedlineGoogle Scholar
- 40 Allen NB, Krefman AE, Labarthe D, Greenland P, Juonala M, Kähönen M, Lehtimäki T, Day RS, Bazzano LA, Van Horn LV, et al. Cardiovascular health trajectories from childhood through middle age and their association with subclinical atherosclerosis. JAMA Cardiol. 2020; 5:557–566. DOI: 10.1001/jamacardio.2020.0140.CrossrefMedlineGoogle Scholar
- 41 Zhang R, Xie J, Yang R, Li R, Chong M, Zhang X, Chen S, Wu S, Yang Y. Association between ideal cardiovascular health score trajectories and arterial stiffness: the Kailuan Study. Hypertens Res. 2020; 43:140–147. DOI: 10.1038/s41440‐019‐0341‐4.CrossrefMedlineGoogle Scholar