Cardiovascular risk scores in the prediction of subclinical atherosclerosis in young adults: evidence from the cardiovascular risk in a young Finns study

Abstract

Aim

To study the utility of risk scores in the prediction of subclinical atherosclerosis in young adults.

Methods and results

Participants were 2204 healthy Finnish adults aged 24–39 years in 2001 from a population-based follow-up study Cardiovascular Risk in Young Finns. We examined the performance of the Framingham, Reynolds, Systematic Coronary Risk Evaluation (SCORE), PROCAM, and Finrisk cardiovascular risk scores to predict subclinical atherosclerosis, that is carotid artery intima-media thickness (IMT) and plaque, carotid artery distensibility (CDist), and brachial artery flow-mediated dilatation (FMD) 6 years later. In a 6-year prediction of high IMT (highest decile or plaque), areas under the receiver operating characteristic curves (AUC) for baseline Finrisk (0.733), SCORE (0.726), PROCAM (0.712), and Reynolds (0.729) risk scores were similar as for Framingham risk score (0.728, P always ≥ 0.15). All risk scores had a similar discrimination in predicting low CDist (lowest decile) (0.652, 0.642, 0.639, 0.658, 0.652 respectively, P always ≥ 0.41). In the prediction of low FMD (lowest decile), Finrisk, PROCAM, Reynolds, and Framingham scores had similar AUCs (0.578, 0.594, 0.582, 0.568, P always ≥ 0.08) and SCORE discriminated slightly better (AUC = 0.596, P<0.05). The prediction of subclinical outcomes was consistent when estimated from other statistical measures of discrimination, reclassification, and calibration.

Conclusion

Cardiovascular disease risk scores had equal value in predicting subclinical atherosclerosis measured by IMT and CDist in young adults. SCORE was more accurate in predicting low FMD than Framingham risk score.

Keywords

cardiovascular risk score subclinical atherosclerosis ultrasound

Introduction

Atherosclerosis begins early in life. Lesions progress with age but the rate of progression and vulnerability to acute events varies between individuals [1]. Single cardiovascular risk factors have modest capabilities of predicting development of cardiovascular disease (CVD) [2], but risk prediction can be improved by incorporating risk factors into risk prediction models, such as the Framingham risk score [1]. Risk scores are often used to stratify individuals into different groups based on their risk of developing cardiovascular events. As CVD has a long asymptomatic phase, there has been support for the expansion of predictive studies of arterial disease from its clinical form to subclinical manifestation [3]. Detection of subclinical atherosclerosis has been shown to be a useful method for the prediction of future coronary events [4, 5].

Primary prevention of CVD has a clear clinical demand, as 50% of men and 64% of women who died suddenly of coronary heart disease (CHD) in the United States had no earlier symptoms [6]. Currently, clinicians may consider estimating CVD risk in patients with Framingham or Systematic Coronary Risk Evaluation (SCORE) risk scores using data on lipids, blood pressure and smoking in which elevated risk would be an indication for intervention [7]. Other risk prediction models have also been developed, including the Finrisk score [8], the Reynolds risk score [9, 10], and PROCAM http://www.chd-taskforce.com. These scores have been used to estimate the probability of clinically evident arterial disease, primarily in mid life and among the elderly [11, 12]. However, given that subclinical changes have developed by young adulthood, examining the association between CVD risk scores and the extent and severity of subclinical atherosclerosis at early stage is of clinical importance.

In this analysis of the Cardiovascular Risk in Young Finns study, we examine capabilities of the Framingham, SCORE, Finrisk, PROCAM, and Reynolds cardiovascular risk scores in prediction of subclinical atherosclerotic outcomes in young adults measured by carotid artery intima–media thickness (IMT) and plaque, carotid artery distensibility (CDist), and brachial artery flow-mediated dilatation (FMD).

Methods

Patients

The Cardiovascular Risk in Young Finns study is a multicentre follow-up study of cardiovascular risk from childhood to adulthood. The study began in 1980 when 3596 Finns aged 3–18 years participated in the first cross-sectional survey. In adulthood, follow-ups have been performed in 2001 and 2007. Vascular ultrasound studies were carried out for 2265 study participants aged 24–39 years in 2001, and for 2197 participants aged 30–45 years in 2007. Of these, 1803 participants had vascular ultrasound available at both time-points and among them the use of lipid-lowering (N = 7) and antihypertensive medication (N = 43) was rare. The participants gave written informed consent in 2001 and 2007. The study complies with the Declaration of Helsinki and the research protocol was approved by local ethics committee.

Physical examination and self-report measures

Height and weight were measured, and body mass index was calculated as weight (kg)/height (m²) [13]. Blood pressure was measured at least three times with a random zero sphygmomanometer. Smoking status and family history of CVD were ascertained from self-report questionnaires. Participants smoking on a daily basis were considered smokers. Parental history of myocardial infarction was reported less than 55 years in either parent in 2001 and less than 55 years in men and less than 65 years in women in 2007. A history of stroke was reported at any age.

Blood biochemistry

Venous blood samples were drawn primarily from the right antecubital vein after an overnight fast and serum separated, aliquoted and stored at −70°C until analysis. Determination of serum triglycerides, total cholesterol and HDL-cholesterol was done as described earlier [13]. LDL-cholesterol was estimated by the Friedewald formula [14] in participants with triglyceride levels of less than 4.0 mmol. Serum C-reactive protein (CRP) was determined turbidimetrically (2001: CRP-UL reagent, Wako, USA, 2007: CRP Latex reagent, Olympus, Ireland) on an AU400 analyzer (Olympus, Japan).

Ultrasound measurements

Ultrasound studies were performed on the carotid and brachial arteries with Acuson Sequoia512 ultrasound mainframes (Acuson, Mountain View, California, USA) with a 13.0 MHz linear array transducer by the same single measurer in both follow-ups.

Common carotid IMT was measured on the posterior wall of the left common carotid artery approximately 10 mm proximal to the carotid bifurcation. A minimum of four measurements were taken to calculate mean carotid IMT. The left common carotid artery and carotid bulb area were also scanned for atherosclerotic plaques, defined as distinct areas of the far and near vessel walls protruding into the lumen greater than 50% of the adjacent intima–media layer [15].

CDist was assessed by measuring the common carotid artery diameter in end-diastole and end-systole. The proportional change between systolic and diastolic values was calculated and distensibility was expressed as the ratio between change in diameter and pulse pressure derived from concomitant brachial blood pressure [15]. CDist = [(systolic diameter–diastolic diameter)/diastolic diameter]/pulse pressure.

Brachial FMD was examined by measuring the left brachial artery diameter both at rest and during reactive hyperemia. The increased blood flow was induced by inflating a pneumatic tourniquet placed around the forearm to a pressure of 250 mmHg for 4.5 min and then deflating the tourniquet [15]. Measurement of arterial diameter was made at end-diastole at a fixed distance from an anatomic marker at rest and 40, 60, and 80 s after cuff release [15]. The maximum vessel diameter during dilatation was expressed as the percentage relative to resting scan.

Adverse outcome was determined as the highest decile (≥ 90th percentile) and/or carotid plaque for IMT (high IMT) and lowest decile (≤ 10th percentile) for CDist and FMD (low CDist and low FMD) in 2007.

In 2001, 57 participants were reexamined 3 months after their original scan to assess variability in measurements. The between-visit coefficients of variation for IMT, Cdist, and FMD were 6.4, 16.3, and 26.0% respectively [16].

Cardiovascular risk score classifications

We calculated the Framingham Risk score [16], SCORE [17], Finrisk [8], Reynolds Risk score [9, 10], and PROCAM http://www.chd-taskforce.com algorithms for each participant. Framingham and Reynolds estimate 10-year risk of any CVD event; SCORE estimates 10-year risk of fatal CVD event; Finrisk measures 10-year risk of CHD event or stroke and PROCAM estimates 10-year risk of CHD event. All risk scores include age and have different algorithms for men and women. In addition to conventional risk factors included in risk scores (smoking status, systolic blood pressure, total cholesterol, HDL-cholesterol and diabetes status), PROCAM includes parental history of myocardial infarction, LDL-cholesterol, triglycerides, and data on antihypertensive medication. Finrisk includes parental history of myocardial infarction or stroke and Reynolds Risk score includes parental history of premature myocardial infarction and high-sensitivity plasma CRP [9, 10]. SCORE algorithm excluded diabetes status and HDL-cholesterol [17, 18].

Statistical methods

Association between risk scores and ultrasound measurements

Cross-sectional (2001 and 2007) and longitudinal associations between risk scores and ultrasound measurements were examined with Spearman's correlations. These statistical analyses were performed using SAS (version 9.1.3; SAS Inc., Cary, North Carolina, USA) software and statistical significance inferred at a two-tailed P value of less than 0.05.

Comparison of baseline risk scores to predict 6-year subclinical atherosclerosis

We compared the utility of baseline risk scores to predict 6-year subclinical atherosclerosis. These analyses were based on separate logistic models that included a single binary subclinical outcome with a single risk score as the predictor variable. For comparison, we used Framingham as the reference risk score, with subsequent comparisons made between Framingham with Finrisk, SCORE, PROCAM, or Reynolds. A number of criteria put forward by the American Heart Association [19], were used to compare values between risk scores. The following was performed using STATA 10.1 (STATA Corp., College Station, Texas, USA)

Model calibration

Calibration of each model within groups (tenths) was assessed using the Hosmer–Lemshow (H–L) χ² statistic [20]. Values greater than 20 (P < 0.01) suggest a lack of adequate calibration [21].

Model discrimination

Discrimination was estimated using area under the receiver operating characteristic curve (AUC) determined for each logistic regression model. Differences in AUC between Framingham and Finrisk, SCORE, PROCAM, or Reynolds risk score models were estimated using the DeLong algorithm [22].

Risk reclassification of 6-year subclinical atherosclerosis

Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to determine the extent to which Finrisk, SCORE, PROCAM or Reynolds risk scores compared with the Framingham score reassigned participants to a risk level or category that better reflected their final outcome status (case or control) [23, 24]. For NRI, participants were assigned to four categories reflecting their 6-year risk of the subclinical outcome. IDI is the continuous version of NRI in which, instead of assigning categories of risk, differences between risk probabilities for the two models are averaged and differenced for cases and controls. A P ≤ 0.01 for IDI comparisons suggests improved model performance [23].

Results

Table 1 presents descriptive data on study participants. Figure 1a and b display cumulative distributions of 10-year risk of CVD event according to Framingham, SCORE, Finrisk, PROCAM, and Reynolds risk scores in 2001 for men and women.

Association between risk scores and ultrasound measurements

Table 2 displays Spearman's correlation between 10-year risk scores and ultrasound measurements. For IMT and CDist, all correlations were significant (P always < 0.001). Correlations for FMD were not consistent and either non-significant or low (−0.07 ≤ r ≤ 0.09).

Comparison of baseline risk scores to predict 6-year subclinical atherosclerosis

Table 3 displays model fit, discrimination, calibration and reclassification indices. For the outcome of high carotid IMT or plaque, Finrisk, SCORE and Reynolds risk scores tended to perform equally well as Framingham, but calibration was best for the Reynolds model (lack of fit remained substantial however). PROCAM showed reduced AUC, NRI, and IDI in comparison with Framingham, but only IDI was statistically significant. Finrisk performed equally with Framingham in predicting 6-year low CDist, whereas reclassification was less accurate for SCORE (IDI) and PROCAM (NRI). Although discrimination was similar between Framingham and Reynolds risk scores, NRI and IDI were more accurate when the model with Reynolds was used. For the prediction of 6-year low FMD, Finrisk and Reynolds risk scores tended to perform equally well. SCORE improved discrimination over Framingham risk score (AUC 0.596 vs. 0.568), but calibration was poorer (18.7 vs. 4.4). Reclassification was less accurate when PROCAM was used in place of Framingham to predict low FMD.

Table 1

Descriptive data in 2001 and 2007

	Men (%)	Women (%)
Year 2001
N	794	1015
Age (years)	31.9	31.9
BMI (kg/m²)	25.7 ± 4.0	24.4 ± 4.5
SBP (mmHg)	12 ± 12	112 ± 12
DBP (mmHg)	73 ± 11	69 ± 10
Total cholesterol (mmol/l)	5.19 ± 0.95	5.08 ± 0.93
LDL-cholesterol (mmol/l)	3.43 ± 0.92	3.16 ± 0.76
HDL-cholesterol (mmol/l)	1.15 ± 0.27	1.41 ± 0.31
Triglycerides (mmol/l)	1.50 ± 0.95	1.17 ± 0.69
C-reactive protein (mg/l)	1.45 ± 3.31	2.25 ± 4.42
Smokers (N)	209 (26.3)	177 (17.4)
Diabetics (N)	6 (0.8)	6 (0.6)
Family history of CVD (N)	76 (9.6)	86 (8.5)
IMT (mm)	0.59 ± 0.10	0.57 ± 0.08
CDist (%/ 10mmHg)	2.33 ± 0.66	2.00 ± 0.77
FMD (%)	6.84 ± 4.04	8.78 ± 4.50
Framingham (%)	2.53 ± 2.65	1.00 ± 1.02
SCORE (%)	0.22 ± 0.25	0.02 ± 0.02
Finrisk (%)	0.91 ± 0.81	0.41 ± 0.25
PROCAM (%)	1.69 ± 2.31	0.12 ± 0.13
Reynolds (%)	0.66 ± 0.62	0.21 ± 0.19
Year 2007
N	794	1015
Age (years)	37.9	37.9
BMI (kg/m²)	26.7 ± 4.2	25.3 ± 4.9
SBP (mmHg)	126 ± 13	116 ± 14
DBP (mmHg)	79 ± 11	73 ± 11
Total cholesterol (mmol/l)	5.19 ± 0.95	4.94 ± 0.86
LDL-cholesterol (mmol/l)	3.28 ± 0.82	2.96 ± 0.72
HDL-cholesterol (mmol/l)	1.21 ± 0.28	1.45 ± 0.33
Triglycerides (mmol/l)	1.63 ± 1.05	1.18 ± 0.63
C-reactive protein (mg/l)	1.48 ± 2.63	2.02 ± 3.56
Smokers (N)	167 (21.1)	150 (14.9)
Diabetics (N)	12 (1.5)	11 (1.1)
Family history of CVD (N)	76 (9.6)	86 (8.5)
IMT (mm)	0.64 ± 0.11	0.61 ± 0.09
CDist (%/10mmHg)	1.73 ± 0.61	2.03 ± 0.73
FMD (%)	7.44 ± 3.71	9.84 ± 4.83
Framingham (%)	4.73 ± 4.00	1.94 ± 1.78
SCORE (%)	0.57 ± 0.53	0.07 ± 0.08
Finrisk (%)	1.56 ± 1.23	0.64 ± 0.41
PROCAM (%)	2.59 ± 2.96	0.20 ± 0.23
Reynolds (%)	1.37 ± 1.11	0.34 ± 0.31

Data are mean ± SD for continuous variables or N (%) for dichotomous variables. CDist, carotid artery distensibility; CVD, cardiovascular disease; DBP, diastolic blood pressure; FMD, flow-mediated dilatation; IMT, intima–media thickness; SBP, systolic blood pressure; SCORE, Systematic Coronary Risk Evaluation.

Discussion

Current guidelines for primary prevention of CVD recommend initial assessment and risk stratification based on traditional risk factor scoring followed by therapeutic intervention when necessary [1]. However, risk scores have been developed to predict risk of clinically evident CVD rather than subclinical changes. In our study, we found significant correlations between CVD risk scores and markers of subclinical atherosclerosis. According to AUCs, all risk scores seemed to have equal performance in the prediction of high IMT and carotid plaque (P always ≥ 0.15).

Of the subclinical outcomes that were examined, low FMD was least well predicted by any of the risk scores.

All risk scores presented lack of calibration in predicting high IMT and low Cdist, whereas prediction of low FMD did not display similar inadequacy. These findings could be partially accounted for high variability in FMD [25]. However, SCORE displayed higher discrimination in prediction of low FMD than Framingham. Moreover, Finrisk, PROCAM, and Reynolds risk scores had higher AUC than Framingham, although none of these differences were statistically significant. Thus, Framingham risk score might have lower value in the prediction of early endothelial dysfunction. To some extent, observation could be because of the lack of family history of CVD in Framingham compared with Finrisk, PROCAM, and Reynolds. However, the significantly more discriminatory SCORE also lacked a family history of CVD. Moreover, Finrisk and PROCAM displayed adverse reclassification compared with Framingham. Thus, the addition of family history may not substantially improve the prediction of low FMD.

All risk algorithms included age, smoking status, systolic blood pressure, total cholesterol, HDL-cholesterol, and diabetes status except SCORE, which excluded HDL-cholesterol and diabetes [8, 16, 17]. Moreover, Reynolds risk score included high-sensitivity plasma CRP measurement and parental history of premature CVD [9, 10]. Parental history of myocardial infarction and stroke was included in the Finrisk score [8] and PROCAM included LDL-cholesterol, triglycerides, parental history of myocardial infarction, and regional adjustment factor based on geographic prevalence of CVD http://www.chd-taskforce.com. In this study, all risk scores performed equally in the prediction of 6-year high IMT and low CDist, suggesting that the additional risk factors included in Finrisk, Reynolds, and PROCAM risk scores did not increase discrimination in our cohort. Moreover, although lacking HDL-cholesterol and diabetes status, SCORE showed equal value in predicting high IMT and better discrimination in prediction of low FMD than Framingham risk score, which could be partially accounted for the European basis of SCORE.

Although Finrisk was based on a Finnish cohort and SCORE based on a European cohort, there were few differences between these and the US-based Framingham risk score in the prediction of subclinical atherosclerosis in our Finnish cohort. PROCAM risk score included regional adjustment factors because of geographical differences in the prevalence of CHD http://www.chd-taskforce.com. However, PROCAM showed a similar value as the Framingham risk score. Seemingly, nationality had little interfering effect on risk estimation in our analyses.

Table 2

Spearman's correlation between 10-year CVD risk scores and ultrasound measurements

	2001–2001	2007–2007	2001–2007	2001–2001	2007–2007	2001–2007	2001–2001	2007–2007	2001–2007
Men
Finrisk	0.30^∗∗∗	0.37^∗∗∗	0.39^∗∗∗	−0.33^∗∗∗	−0.25^∗∗∗	−0.25^∗∗∗	0.02	−0.02	−0.008
SCORE	0.33^∗∗∗	0.38^∗∗∗	0.40^∗∗∗	−0.34^∗∗∗	−0.26^∗∗∗	−0.26^∗∗∗	0.01	−0.05	−0.02
Reynolds	0.35^∗∗∗	0.40^∗∗∗	0.42^∗∗∗	−0.39^∗∗∗	−0.28^∗∗∗	−0.28^∗∗∗	0.01	−0.03	−0.02
PROCAM	0.27^∗∗∗	0.33^∗∗∗	0.38^∗∗∗	−0.31^∗∗∗	−0.22^∗∗∗	−0.23^∗∗∗	0.02	−0.01	0.006
Women
Framingham	0.29^∗∗∗	0.39^∗∗∗	0.35^∗∗∗	−0.33^∗∗∗	−0.31^∗∗∗	−0.30^∗∗∗	0.07^∗	0.002	−0.04
Finrisk	0.28^∗∗∗	0.38^∗∗∗	0.32^∗∗∗	−0.24^∗∗∗	−0.25^∗∗∗	−0.24^∗∗∗	0.04	−0.01	−0.04
SCORE	0.32^∗∗∗	0.37^∗∗∗	0.35^∗∗∗	−0.31^∗∗∗	−0.33^∗∗∗	−0.32^∗∗∗	0.05	−0.03	−0.07^∗
Reynolds	0.26^∗∗∗	0.38^∗∗∗	0.32^∗∗∗	−0.34^∗∗∗	−0.32^∗∗∗	−0.27^∗∗∗	0.09^∗∗	0.03	−0.002
PROCAM	0.28^∗∗∗	0.39^∗∗∗	0.32^∗∗∗	−0.27^∗∗∗	−0.27^∗∗∗	−0.24^∗∗∗	0.08^∗∗	0.02	−0.02

2001–2001, correlation between CVD risk score in 2001 and ultrasound measurement in 2001. 2001–2007, correlation between CVD risk score in 2001 and ultrasound measurement in 2007. 2007–2007, correlation between CVD risk score in 2007 and ultrasound measurement in 2007. SCORE, Systematic Coronary Risk Evaluation.

^∗ P<0.05.

^∗∗ P<0.01.

^∗∗∗ P<0.001.

Limitations

Risk scores were originally designed for the prediction of myocardial infarction or CHD death over a 10-year period [8, 16, 17] apart from Reynolds, which predicts 10-year cardiovascular event risk [9, 10]. Data on CVD end-points were unavailable in our cohort aged 30–45 years. Instead, we used markers of subclinical atherosclerosis, that is, high carotid IMT, low carotid distensibility and low brachial FMD in the 2007 follow-up study as outcome variables. Of these, most consistent data concerning associations with CVD events are available on IMT. In a review by Lorenz et al. [26], it was shown that IMT is a strong predictor of CVD events independent of conventional risk factors. However, in the Rotterdam study [27] IMT measurements did not statistically significantly improve predictive value when added to conventional risk factor data. Reynolds risk score for women included hemoglobinA_1C levels for diabetics. However, hemoglobinA_1C was not measured in the Young Finns study and thus, the effect of diabetes was omitted from the analyses. The effect of exclusion, if anything, is likely to be small because of the number of diabetic men and women (N = 6 in 2001 and N = 11 in 2007). In addition, Reynolds and PROCAM risk scores included myocardial infarction less than 60 years in either parent as parental history of CVD. In Young Finns, parental history of CVD was classified as myocardial infarction less than 55 years in either parent in 2001 and less than 55 years in men and less than 65 years in women in 2007.

Table 3

Model fit, discrimination, calibration, and reclassification indices for prediction of 6-year subclinical outcomes from year 2001 risk scores

	Framingham	Finrisk		SCORE		PROCAM		Reynolds
Outcome	Statistic	Statistic	P value^∗	Statistic	P value^∗	Statistic	P value^∗	Statistic	P value^∗
High carotid IMT (>90^th percentile) or plaque
OR	1.7	1.7	–	1.7	–	1.5	–	1.7	–
(95% CI)^a	(1.5–2.0)	(1.5–2.0)		(1.5–1.9)		(1.3–1.7)		(1.5–1.9)
AUC	0.728	0.733	0.41	0.726	0.83	0.712	0.15	0.729	0.95
(95% CI)	(0.698–0.758)	(0.702–0.763)		(0.695–0.757)		(0.681–0.744)		(0.698–0.759)
H–L	51.3	60.6	–	54.7	–	57.2	–	46.3	–
NRI	–	2.2%	0.25	3.7%	0.18	−4.9%	0.12	3.9%	0.16
IDI	–	−0.17%	0.29	−0.90%	0.02	−2.76%	<0.01	−0.10%	0.41
Low CDist (<10^th percentile)
OR	1.4	1.3	–	1.3	–	1.2	–	1.4	–
(95% CI)^a	(1.2–1.5)	(1.2–1.5)		(1.1–1.4)		(1.1–1.4)		(1.3–1.6)
AUC	0.652	0.652	0.97	0.642	0.45	0.639	0.41	0.658	0.54
(95% CI)	(0.612–0.692)	(0.611–0.693)		(0.603–0.681)		(0.598–0.680)		(0.618–0.697)
H–L	25.8	29.9	–	27.8	–	27.2	–	25.1	–
NRI	–	1.9%	0.32	−3.8%	0.22	−27.4%	<0.01	6.9%	0.04
IDI	–	−0.05%	0.42	−0.50%	0.005	−0.75%	0.01	0.78%	0.003
Low brachial FMD (<10^th percentile)
OR	1.2	1.2	–	1.2 (1.1–1.4)	–	1.1	–	1.2	–
(95% CI)^a	(1.1–1.4)	(1.1–1.4)				(1.0.–1.3)		(1.1–1.4)
AUC	0.568	0.578	0.13	0.596	<0.05	0.594	0.08	0.582	0.09
(95% CI)	(0.521–0.615)	(0.531–0.624)		(0.550–0.642)		(0.548–0.639)		(0.535–0.629)
H–L	4.4	8.0	–	18.7	–	19.1	–	8.8	–
NRI	–	−1.5%	0.35	−7.1%	0.10	−13.6%	<0.01	5.1%	0.08
IDI	–	−0.29%	0.01	−0.22%	0.10	−0.50%	<0.01	−0.08%	0.27

Analyses for each outcome were limited to patients that had sufficient risk variables to calculate all risk scores (IMT, N=1761; CDist, N=1754; FMD, N=1751). Note: population mean for high IMT/plaque = 15.5%. Risk categories, of <14%, 14–16%, 16–20%, ≥ 20% used for calculation of NRI. Population mean for low CDist = 10.2%. Risk categories, of <9%, 9–11%, 11–15%, ≥ 15% used for calculation of NRI. Population mean for low FMD = 10.3%. Risk categories, of <9%, 9–11%, 11–15%, ≥ 15% used for calculation of NRI. AUC, area under receiver-operating characteristic curve; CDist, carotid artery distensibility; CI, confidence interval; FMD, flow-mediated dilatation; H–L, Hosmer–Lemeshow χ² statistic; IDI, integrated discrimination improvement; IMT, intima–media thickness; NRI, net reclassification improvement; OR, odds ratio, SCORE, Systematic Coronary Risk Evaluation. ^aOdds ratios are expressed for a 1 standard deviation increase in Framingham, Finrisk, SCORE, PROCAM, or Reynolds risk scores. ^∗ P values for comparisons between Framingham (reference risk score) vs. each of Finrisk, SCORE, PROCAM, or Reynolds risk scores.

Clinical implications

CVD risk scores are able to predict future subclinical atherosclerosis in young adults. According to our results, the risk of subclinical atherosclerosis in young adults can be assessed with any of the examined risk scores. As our findings link early adulthood CVD risk to vascular changes, young adults should be motivated to reduce CVD risk at an early stage.

Footnotes

Acknowledgements

This study was financially supported by Tampere and Turku University Hospital Medical Funds, the Academy of Finland (grants. 121 584, 126 925), the Social Insurance Institution of Finland, Research Foundation of Orion Corporation, the Margaretha Foundation, the Lydia Maria Julin Foundation, the Juho Vainio Foundation and the Foundation of Outpatient Care Research. Mika Kivimäki is supported by NIH/National Heart, Lung and Blood Institute, USA (grant 2R01HL036310-20A2).

There are no conflicts of interest.

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