Cardiovascular Risk Factors From Childhood and Midlife Cognitive Performance

Cardiovascular Risk Factors From Childhood and Midlife Cognitive Performance

JACC VOL. 70, NO. 15, 2017 Letters OCTOBER 10, 2017:1940–7 Please note: The authors have reported that they have no relationships relevant to the c...

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JACC VOL. 70, NO. 15, 2017


OCTOBER 10, 2017:1940–7

Please note: The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

(Young Finns Study). They observed that the cumulative burden of cardiovascular risk factors (i.e., from childhood or adolescence) was associated with worse


midlife cognitive performance independent of adult-

1. Brownfoot FC, Gagliardi DI, Bain E, Middleton P, Crowther CA. Different corticosteroids and regimens for accelerating fetal lung maturation for women at risk of preterm birth. Cochrane Database of Systematic Reviews 2013;8: CD006764. 2. Baud O, Maury L, Lebail F, et al. Effect of early low-dose hydrocortisone on survival without bronchopulmonary dysplasia in extremely preterm infants (PREMILOC): a double-blind, placebo-controlled, multicentre, randomised trial. Lancet 2016;387:1827–36.

hood exposure. The authors examined the effects of exposures to early-life






recommended guidelines. The reasoning behind this analysis is interesting, and while this approach is very informative, we believe that a better approach would be to examine trajectories of risk factors exceeding

3. Aye CYL, Lewandowski AJ, Lamata P, et al. Disproportionate cardiac hypertrophy during early postnatal development in infants born preterm. Pediatr


Res 2017;82:36–46.

approach would be more suitable for capturing the

4. Dalziel SR, Walker NK, Parag V, et al. Cardiovascular risk factors after antenatal exposure to betamethasone: 30- year follow-up of a randomised controlled trial. Lancet 2005;365:1856–62. 5. Leeson P, Lewandowski AJ. A new risk factor for early heart failure: preterm birth. J Am Coll Cardiol 2017:2643–5.





effect of risk exposure all throughout childhood, adolescence, and young adulthood (2). By classifying participants into distinct, mutually exclusive trajectory groups, this method would allow for closer scrutiny of the population heterogeneity in the change of cardiovascular risk factor exposures over the life course. This would then allow for the direct

Cardiovascular Risk Factors From Childhood and Midlife Cognitive Performance

comparison of outcomes across these groups. Furthermore, Rovio et al.’s (1) use of the area under the curve (AUC) to indicate the long-term burden raises some questions. How would the authors deal with 2 participants when the initial value of 1 corresponds to the final value of the other? And vice versa? In fact, an identical AUC could be the result of dia-

We were very interested while reading the work of

metrically opposite evolution: a same total AUC

Rovio et al. (1) in which they sought to investigate the


associations between early-life cardiovascular risk

(Figure 1, a). Considering the equal impact of

factors and midlife cognitive performance in the YFS

increasing versus decreasing risk factor exposure

1, aþb) and opposite incremental AUC

F I G U R E 1 Area Under the Curve of Systolic Blood Pressure

Systolic BP (mm Hg)


Individual #1

Overall Growth Curve

Individual #2





50 b


0 0


20 30 Age (Years)




20 Age (Years)








Age (Years)

Diametrically opposite risk factor (systolic blood pressure [BP]) evolution leading to a same total area under the curve (aDb) but an opposite incremental area under the curve (a).


JACC VOL. 70, NO. 15, 2017


OCTOBER 10, 2017:1940–7

while aging raises some concerns, especially because the investigators demonstrated that the impact of the burden of risk factors across ages differs.

F I G U R E 1 Midlife Performance on Episodic Memory and Visual

Associative Learning According to the Number of Early Life and Midlife Cardiovascular Risk Factors (N ¼ 1,622)

Victor Waldmann, MD, MPH *Bamba Gaye, PhD *Sudden Death Expertise Center INSERM U970 Paris Cardiovascular Research Center (PARCC) Cardiology Department European Georges Pompidou Hospital 20 rue Leblanc 75015 Paris France E-mail: [email protected] Please note: Both authors have reported that they have no relationships relevant to the contents of this paper to disclose.


0.50 Mean Visual and Episodic Memory and Visuospatial Associative Learning


N = 25

N = 484

0.25 N = 286

N = 162 N = 98


N = 289

N = 59

N = 98 N = 121






Number of Midlife Cardiovascular Risk Factors

1. Rovio SP, Pahkala K, Nevalainen J, et al. Cardiovascular risk factors from childhood and midlife cognitive performance: the Young Finns Study. J Am Coll Cardiol 2017;69:2279–89. 2. Song M, Hu FB, Wu K, et al. Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies. BMJ 2016;353:i2195.

Number of Early Life Cardiovascular Risk Factors 0



The values represent means and 95% confidence intervals indicating cognitive performance on visual and episodic memory and visuospatial associative learning in subgroups classified according to the number of risk factors in early and

REPLY: Cardiovascular Risk Factors From Childhood and Midlife Cognitive Performance

midlife. The variables showing significant association for cognitive performance (i.e., systolic blood pressure, serum total-cholesterol, and smoking) were included in the variable for the cardiovascular risk factor clustering. In a multivariable

We thank Drs. Waldmann and Gaye for their interest

model, the association between the number of early life

in our publication (1). Our results indicate that higher

cardiovascular risk factors and midlife cognitive performance

cumulative burden of systolic blood pressure, serum total and low-density lipoprotein cholesterol, and

was significant (p ¼ 0.003) after adjustments for age, sex, family income, and the number of midlife cardiovascular risk factors. The reference line is set on the population mean.

smoking in youth associate with worse midlife

A 3-level variable indicating the number of risk factors during

memory and learning independent of midlife expo-

early life was created from the area under the curve (AUC)

sure to the same risk factors. In our study, the area

variables for each cardiovascular risk factor (1 ¼ no risk factors,

under the curve (AUC) approach enabled quantitative modeling of the cardiovascular risk factor burden in

2 ¼ 1 risk factor, 3 ¼ 2 or 3 risk factors), and the categories are indicated with colors in the figure. The AUC variables for blood pressure and serum total cholesterol were dichoto-

childhood (from 6 to 12 years of age), adolescence

mized into high–risk-factor level ($75th percentile) and

(from 12 to 18 years of age), and young adulthood

low–risk-factor level (<75th percentile). Smoking status was

(from 18 to 24 years of age), each including several

dichotomized into smokers and nonsmokers. The dichoto-

measurements for each risk factor. These age win-

mical variables were summed to create the variable indicating

dows were used to detect the age interval when the

the number of risk factors (range: 0 to 3) during early life (6 to 24 years). A similar variable indicating the number of

risk factors start to exert their influence on midlife

cardiovascular risk factors at the time of cognitive testing was

cognitive performance. Virtually similar associations

formed and placed in the x-axis in the figure.

were found for all age windows. In addition to AUC values, we examined whether the effect of early-life risk exposure is attributable to risk factor levels repeatedly exceeding the current age- and sex-specific

factors always within the guidelines had better

guidelines for cardiovascular health (2–4). The partic-

memory and learning than did those exceeding the

ipants were classified into 4 groups according to their

guidelines at least twice on all risk factors.

frequency of exceeding the guidelines. The partici-

Drs. Waldmann and Gaye suggest that a more

pants with early-life (from 6 to 24 years of age) risk

formal trajectory modeling may be a more accurate