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. 2023 Sep;182(9):3893-3906.
doi: 10.1007/s00431-023-05051-8. Epub 2023 Jun 20.

Tracking between cardiovascular-related measures at 4 and 8 years of age in the INMA-Asturias cohort

Affiliations

Tracking between cardiovascular-related measures at 4 and 8 years of age in the INMA-Asturias cohort

Rocío Fernández-Iglesias et al. Eur J Pediatr. 2023 Sep.

Abstract

Identifying cardiovascular-related measures that track from early childhood into later ages may help inform early prevention targets for cardiovascular disease. In this study, the tracking of triglycerides (TG), high-density cholesterol (HDL-c), atherogenic coefficient (AC), waist circumference to height ratio (WC/Height), mean arterial pressure (MAP), and homeostatic model assessment of insulin resistance (HOMA-IR) was examined in the INMA-Asturias cohort between 4 and 8 years of age. The analysis was conducted in 307 children who participated in the INMA-Asturias cohort (Spain) at 4 and at 8 years of age. Quantile regression models were used to evaluate tracking between measures at both ages, with each measure at 8 years as the dependent variable and the rank transformation of the same measure at 4 years as the independent variable. We found a positive association between HDL-c rank at 4 years and higher quantiles of the HDL-c distribution at 8 years, with an increase of 2.93 mg/dL (95% CI: 1.98, 3.87) per decile in the 0.9 quantile. A positive association was also found for WC/Height, with an increase of 0.008 (95% CI: 0.004, 0.012) per decile in the 0.9 quantile. We observed that tracking for AC increased in the higher quantiles of the distribution at 8 years, with an increase of 0.11 (95% CI: 0.09, 0.14) in the 0.6 quantile compared to an effect of 0.15 (95% CI: 0.09, 0.21) in the 0.9 quantile. Conclusions: Adult markers of dyslipidemia and central obesity tracked between ages 4 and 8 years. For AC, tracking increased in the higher quantiles of the distribution. What is Known: • Atherosclerosis begins in early life, so preventive efforts that start in childhood may delay progression to clinical disease. Determine what cardiovascular risk factors track into time since childhood bring the opportunity to identified those subjects at risk for later cardiovascular disease. • The study of risk factors in health populations and, particularly in children, copes with not clear and/or controversial thresholds definition. This makes it challenging to study tracking in pediatric ages. What is New: • Quantile regression is a useful tool for assessing the tracking of risk factors for which there are no clinically meaningful thresholds. The increasing trend observed in the tracking of dyslipidemia suggests the possible difficulty that children with abnormal values at 4 years of age might have in normalizing them in future years. • The findings of this article may help to determine which cardiovascular-related measures could be screened and followed-up in children.

Keywords: Cardiovascular risk; Childhood; Dyslipidemia; Hyperglycemia; Hypertension; Obesity; Quantile regression; Tracking.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the study sample
Fig. 2
Fig. 2
Quantile regression models with cardiovascular-related measure at 8 years as dependent variable and the rank variable of the corresponding cardiovascular-related measure at 4 years as the independent variable, for the quantiles between 0.1 to 0.9, with increments of 0.05, adjusted for maternal age at delivery, maternal level of education, maternal social class, maternal smoking during pregnancy, maternal pre-pregnancy body mass index, paternal body mass index, parental cardiovascular antecedents, child sex, child mean daily energy intake at 4 and 8 years, child weekly out-of-school physical activity time at 4 and 8 years, week of gestation at delivery, weeks of predominant breastfeeding, and child height at 4 and 8 years. Coefficient estimated are calculated with the independent variables in terms of percentiles and they represent the effect on the dependent variable for each 1-decile increase in the independent variable. They are expressed in terms of number of standard deviations of the dependent variable to homogenize the Y-axis scales
Fig. 3
Fig. 3
Quantile regression models with each cardiovascular-related measure at 8 years as dependent variable and the rank of all the cardiovascular-related measures at 4 years as the independent variables, for the quantiles 0.60 and 0.75, adjusted for maternal age at delivery, maternal level of education, maternal social class, maternal smoking during pregnancy, maternal prepregnancy body mass index, paternal body mass index, parental cardiovascular antecedents, child sex, child mean daily energy intake at 4 and 8 years, child weekly out-of-school physical activity time at 4 and 8 years, week of gestation at delivery, weeks of predominant breastfeeding, and child height at 4 and 8 years. Coefficient estimated are calculated with the independent variables in terms of percentiles and they represent the effect on the dependent variable for each 1-decile increase in the independent variable. They are expressed in terms of number of standard deviations of the dependent variable to homogenize the X-axis scales

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