Highlights
· Prematurity and low birth weight increase later cardiovascular risk.
· In small-for-gestational-age children treated with recombinant human growth hormone, no clinically relevant rise in hypertension was found.
· Only subtle increases in diastolic blood pressure were observed, mainly influenced by overweight and prepubertal status.
Introduction
Prematurity and low birth weight are known risk factors for the development of cardiovascular disease and hypertension (HTN) later in life [1-3]. Several etiopathogenic theories suggest that both conditions impair normal nephrogenesis, leading to reduced nephron endowment and a smaller total glomerular surface area. This diminished filtration capacity compromises sodium excretion, promoting extracellular fluid expansion and elevated blood pressure (BP) [4-6].
One of the primary causes of prematurity is intrauterine growth restriction (IUGR), which is linked to low birth weight for gestational age, short stature during childhood, and an increased risk of HTN and metabolic syndromes in adolescence and adulthood. In particular, the onset of metabolic syndrome and HTN are especially related to an early catch-up or exaggerated statureponderal recovery [7,8]. When the expected height catch-up does not occur, treatment with recombinant human growth hormone (rhGH) is indicated in small-for-gestational-age (SGA) children. It is known that rhGH influences sodium retention and vascular reactivity, both involved in BP regulation [9]. Different observational studies have reported a potential association between childhood rhGH treatment (for various growth hormone [GH]-related indications) and increased cardiovascular events (e.g., ischemic heart disease, subarachnoid or intracerebral hemorrhage) in early adulthood without significant differences in the incidence of HTN [10,11]. However, findings remain inconsistent and are limited by heterogeneous populations and relatively young cohorts [10]. Notably, few studies to date have used ambulatory BP monitoring in children to determine the relationship between childhood rhGH treatment and changes in BP in this patient population [12].
This study aimed to detect potential changes in ambulatory BP by in-office and ambulatory blood pressure monitoring (ABPM) records in SGA patients who underwent treatment with growth hormone by comparison with normal healthy controls.
Methods
1. Study design and recruitment
The study was designed as an observational case-control study. The case group comprised individuals classified as SGA all under treatment with growth hormone. The control group consisted of healthy children with normal neonatal anthropometry who were not receiving any treatment.
SGA was defined as an individual having a birth weight or length below 2 standard deviations (SDs) for the mean established for their gestational age [6]. Among these patients, those who presented with alterations in their placental and/or fetal Doppler records were also defined as exposed to IUGR, while those born before 37 weeks of pregnancy were considered preterm [6].
Criteria to receive rhGH were as follows: (1) Length and/or weight less than 2 SD at birth. (2) No catch-up at 4 years old. (3) Height below 2.5 SDS at the time.
SGA patients treated with rhGH were recruited at the pediatric endocrinology consultation at a tertiary hospital. Healthy controls were randomly selected from several primary care health centers in the area.
Inclusion criteria were as follows: (1) Age between 6 and 16 years at the time of the study. (2) Parental consent provided. In addition, minors aged 12 years or older were asked to give individual assent to research. (3) For the case group: individuals undergoing growth hormone treatment or had just completed it within the last 6 months.
Exclusion criteria were as follows: (1) Diagnosis of kidney disease, cardiovascular disease, or HTN before the study. (2) Any chronic medical treatment including steroids and cardiovascular drugs.
2. Sample size
Sample size was calculated using the dependent variable office DBP SDS, with the formula used to detect differences between 2 independent means under the hypothesis of nonequality. Based on the results reported in the literature [4], it was considered that the mean office DBP in the control group might be 0.06 SDS and 0.86 SDS in the cases, with a combined standard deviation of 0.96 SDS. Additionally, the proportion of the control group was adjusted to 34% of the total sample due to the possible difficulty in recruiting controls. Therefore, to achieve a power of 85%, with a significance level of 0.05 and adding a drop-out rate of 10%, it was calculated that at least 24 controls and 46 cases, totaling 70 participants, would be necessary. Calculations were performed using the software Ene 3.0.
3. Study protocol
All patients were evaluated during one medical visit. Interviewing was conducted by 2 medical doctors. Data on personal or family history of cardiovascular and/or renal diseases, perinatal history, length of breastfeeding, and growth hormone were extracted from interviews along with revision of medical files. All patients underwent a complete anthropometric examination (Elegans Plus stadiometer, Asmedic, S.L.U, Spain) in addition to a physical exam that included the stage of pubertal development (Tanner scale). The patient’s BP was monitored in-office by an oscillometric approach (Welch Allyn Connex 3400 SureBP, Hillrom, USA). We recorded 3 measurements under resting conditions. In addition, all patients underwent an ambulatory continuous BP monitoring (ABPM) study for 24 hours (Spacelabs 90227 oscillometer, SpaceLabs Medical Inc., USA). BP readings were taken every 20 minutes during the day and every 30 minutes at night. The night period was defined from 10:00 PM until 6:00 AM. The daytime period was defined as 7:00 AM until 9:00 PM. Recordings with ≥70% successful readings, ≥1 records per hour, and 40 total readings per day were considered valid and were included in the analysis. The periods of patient wakefulness and sleep were reported through a diary. All participants were instructed to continue with their routine lives. Blood analysis was performed only in the case group, including serum concentrations of creatinine (IDMStraceable Jaffe assay), glucose (hexokinase assay), and insulin (immunoassay approach on the Siemens® Immulite platform). The estimated glomerular filtration rate (eGFR) was calculated by the CKiD Under 25 (U25) equation [13]. The homeostasis model assessment for insulin resistance (HOMA-IR) index was calculated using the following relationship: insulin (U/mL)×glucose (mmol/L)/22.5 [14].
4. Study variables
• Family history of cardiovascular disease (HTN and/or acute myocardial infarction before 60 years of age in a first-degree relative).
• Weight (g) and length (cm) at birth.
• Gestational age (weeks).
• Length of breastfeeding (months). Established breastfeeding was defined as that which lasted more than 3 months.
• Length of growth hormone treatment (months).
• Weight (kg), height (cm), and body mass index (BMI=weight/height²) at the time of the study. A BMI between the 85th and 95th percentile was considered overweight and a BMI equal to or over the 95th percentile was considered obesity [15].
• Tanner pubertal stage: prepubertal (Tanner I), intermediate (Tanner II and III; female breast buds, male testicular volume 4 mL), and advanced (Tanner IV and V).
• Automated in-office BP records (AOBP): BP was measured 3 times, and the mean value of the last 2 measurements of systolic BP (SBP) and diastolic BP (DBP) was included in the analysis.
• Definition of HTN according to AOBP records was systolic or diastolic BP at or above the 95th percentile. In patients over 16 years of age, we used the cutoff proposed by the European Hypertension Guidelines [16].
• ABPM recordings: all were converted to standard deviation scores (SDS) using the reference LMS data (a measure of skewness (L), median (M), and coefficient of variation (S) to normalize the data) published by Wühl et al. [17].
• Overall (24 hours) mean SBP and DBP and mean arterial pressure (MAP) (mmHg)
• Mean diurnal SBP and DBP and MAP (mmHg)
• Mean nocturnal SBP and DBP and MAP (mmHg)
• BP nighttime dipping status (physiological drop in BP) was analyzed by calculating the ratio of MAP during the day to nighttime MAP (MAP daytime/nighttime). Similar to the study by Wühl et al. [17] > 10% decrease in the MAP during the night was considered to be dipping. MAP nighttime dipping of <10% (MAP daytime/nighttime <1.1) was considered to be nondipping.
• Definition of HTN according to ABPM records was mean overall (24 hours) SBP/DBP and/or daytime SBP/DBP and/or nighttime SBP/DBP at or above the 95th percentile.
• HTN was classified as white coat HTN, masked HTN, or sustained HTN according to the classification of the European Society of Hypertension [18]. White coat HTN was diagnosed if office-based pressures were elevated but out-of-office pressures were normal, masked HTN was diagnosed if office-based pressures were normal and out-of-office pressures elevated, and sustained HTN was diagnosed if BP was elevated regardless of setting or circumstance.
• An eGFR > 90 mL/min/1.73 m2 was considered normal according to the CKiD U25 equation [13].
• A HOMA-IR >3.43 was considered high [14].
5. Statistical analysis
Qualitative variables are presented as frequencies and percentages, while quantitative variables are presented as mean±SD or medians and interquartile ranges, depending on the type of distribution, as estimated using the Shapiro-Wilk test. Bivariate analysis was conducted. For normally distributed data, the 2 groups were compared using Student t-test, while for nonnormally distributed data, the Mann-Whitney U-test was used. For qualitative variables, the chi-square or Fisher exact test was used, as appropriate. The SDS of the BP determinations were correlated with other quantitative variables using the Pearson test for parametric cases and the Spearman test for nonparametric cases. A multiple linear regression model was applied for the SDS of the BP determinations that showed statistically significant differences between the case group and the controls in the bivariate analysis. A P-value of <0.05 was considered significant. All statistical analyses were performed using IBM SPSS Statistics ver. 29.0 (IBM Co., USA).
Results
A total of 72 children were finally recruited for the study, including 46 cases and 26 controls. Seven cases were excluded from the case group and 6 cases from the control group due to not meeting the inclusion criteria. The characteristics of the participants are summarized in Table 1.
Both groups were comparable in terms of family history of cardiovascular disease, actual age, sex, and pubertal development. In contrast, significant differences were found when comparing weight and length at birth and at the time of the study, although no differences in the overweight rate at the time of the study were recorded. Gestational age was significantly lower in the study group (41% of those patients were preterm, and 17% were born before 32 weeks of gestation). In the case group, preeclampsia during pregnancy was significantly higher (P=0.011). The length of breastfeeding was longer in the control group (P=0.013). There were no differences in exposure to tobacco in utero.
Only 7 participants (10%) in the whole sample presented with any type of HTN (including white coat and masked HTN), with no significant differences in HTN rate between the groups. In the case group, only 2 children were considered to have sustained HTN, while another 2 were classified as having masked HTN, and another one as having white coat HTN. No patient in the control group was classified as having sustained HTN but 2 patients, both overweight, presented with masked HTN and white coat HTN, respectively. The clinical characteristics of these patients are presented in Supplementary Table 1.
Given the percentage of patients with prematurity, a supplementary comparative analysis was conducted among SGA patients; no differences in BP were observed between SGA patients who were premature and those were full term (Supplementary Table 2).
No differences were observed in the in-office AOBP or ABPM recordings between the 2 groups, except for daytime DBP SDS and 24-hour DBP SDS, both of which were slightly but significantly higher in the SGA group receiving rhGH than the normal control group (Table 2; Fig. 1). The number of patients who showed a MAP nighttime dipping of <10% (nondipping patients) was similar in both groups (30% and 35%, respectively).
A bivariate analysis was performed to analyze possible factors that could have affected the 24-hour DBP and daytime DBP SDS recordings (Table 3). Independent and inverse correlations were found for gestational age, weight at birth, and length at birth. In addition, the presence of overweight at the time of the study was related to higher 24-hour DBP SDS and daytime DBP SDS. In contrast, subjects (cases and controls) who breastfed longer than 3 months showed lower 24-hour DBP SDS. Subjects in the prepubertal stage had higher 24-hour DBP SDS than those in the pubertal stage.
A stratified analysis was performed to analyze the possible influence of sex and stage of puberty on BP recordings. Male prepubertal cases showed a higher 24-hour DBP SDS than male pubertal cases (0.34±0.68 vs. -0.50±1.25, P=0.029; effect size, -0.884). None of these differences were observed in males from the control group or females from either group.
In a multiple linear regression model for the SDS of the 24-hour and daytime DBP recordings (Table 4), being SGA and receiving GH was associated with an increase of 0.451 in the 24-hour DBP SDS (P=0.032) and of 0.462 in the daytime DBP SDS (P=0.042). Notably, being overweight was associated with an increase of 0.950 in the 24-hour DBP SDS (P=0.002) and 1.005 in the daytime DBP SDS (P=0.001). Meanwhile, the 24-hour DBP SDS value increased by 0.499 for patients in the prepubertal stage. These results were normally distributed for the linear regression models (Supplementary Fig. 1).
One last comparative study (Supplementary Table 3) was carried out among SGA patients who had a history of IUGR (N=10) or not (N=36). Differences were only found for gestational age (GA) and birth weight SDS but not BP recordings or other variables.
The average length of treatment with rhGH for all SGA patients was 5 (range, 0.6–12) years; after that, most patients (78%) had achieved height recovery above 2 SDS. After a correlation analysis, the length of rhGH treatment was not found to affect the SDS values of any of the BP measurements.
All SGA patients with GH treatment presented with an eGFR >80 mL/min/1.73 m². The HOMA-IR for insulin resistance was high in 28% of them, with no differences between IUGR and non-IUGR groups. No association was found between the HOMA-IR and BP SDS records either. Although not significant, a higher rate of an altered HOMA-IR was found among nondipping patients compared with the subgroup of dipping patients (49% vs. 22%, respectively, P=0.171; effect size, 0.214).
Discussion
There is evidence of the appearance of HTN following a history of prematurity in adolescents and young adults [8], particularly in male patients [19,20]. In recent years, there has been growing evidence of early, even prepubertal, changes in the circadian rhythm of BP in children with a history of prematurity and/or low birth weight, predominantly in the form of nocturnal BP elevations [4,21-25]. On the contrary, our study found only subtle changes in those at-risk patients in daytime and 24-hour diastolic BP records. As other authors did, we established an inverse correlation between BP values and patient birth weight [26,27].
In our study, all SGA patients were either undergoing treatment with rhGH or had just completed it less than 6 months before BP recording. The incidence of BP alterations in the current study, despite a mean treatment time with rhGH of 5 years, was very low. In the literature, various cohorts of young adults treated with GH during childhood for different conditions have been studied, with follow-up periods exceeding 10 years after treatment cessation (average treatment duration of 8–9 years) [10,28]. These studies have reported a significant increase in AOBP measurements (both systolic and diastolic) during follow-up without HTN, although final BP values were comparable to patients not treated with rhGH [28,29]. The incidence of cardiovascular events remains debated; some studies have reported an increased risk associated with longer treatment duration, whereas other studies have not found significant differences—likely reflecting the still relatively young age of the populations studied [10]. In children, one study investigating rhGH treatment (for various indications, with only one SGA patient) reported minimal effects of GH on ABPM [12].
It was speculated that rhGH treatment might induce potential changes in renal function, including an increase in the eGFR, related to elevated levels of insulin-like growth factor 1 that stimulates the renin-angiotensin system. However, this increase in the eGFR was not observed in this study nor previous research [19,21,24,29-31]. Surprisingly, Rakow et al. [32] found a discrete eGFR decrease in a preterm treated with rhGH.
Curiously, we observed that at the prepubertal stage, SGA boys showed higher 24-hour DBP SDS values than pubertal boys, but females did not show differences according to their pubertal status. However, previous research had indicated that adolescent girls born prematurely have a higher risk of diastolic HTN [33], which was attributed to hormonal effects on the renin-angiotensin system [34]. In the current study, we did not find any relation with presumed estrogenic status; rather, our results suggest that early elevation of androgen levels could play a role in BP recordings, considering that it is known that from an early age men born prematurely exhibit higher androgen levels than those born at term [35].
Among the various risk factors associated with early alterations in BP, an undeniable one is being overweight. In our study, this factor was associated, regardless of birth weight or GA, with higher daytime diastolic SDS and 24-hour DBP SDS in the ABPM recordings. These findings align with existing evidence indicating that postnatal weight gain, particularly of fat mass, has a more substantial impact on vascular changes than birth weight alone [2]. Previous investigations pointed out the contribution of a high BMI to the occurrence of elevated ABPM readings in SGA children, although at the expense of nocturnal systolic BP elevation [4,25]. Specific clinical recommendations include early monitoring of growth and body composition (e.g., through bioimpedancemetry), promotion of healthy nutrition, encouragement of protective factors such as longer breastfeeding duration—which appeared to reduce BP levels in our study and others—[36] regular physical activity, metabolic screening, and multidisciplinary follow-up.
We did not find any differences between groups in the percentage of nondipping subjects. Some theories claim that possible dysregulation of the autonomic nervous system, accompanied by sympathetic hyper-activation linked to prematurity, could favor the loss of dipping in SGA patients receiving GH therapy, increasing their cardiovascular risk [21,32]. Some studies have linked the loss of dipping to the progression of chronic kidney disease in children [37] or the presence of left ventricular hypertrophy [38] with unknown long-term cardiovascular consequences [39]. In any case, the rate of dipping in our sample was similar to that previously published in SGA and non-SGA children, suggesting a lack of effect of rhGH treatment on this phenomenon [20-26,30].
We analyzed the presence of insulin resistance in our SGA rhGH patients. HOMA-IR was elevated in almost one-third of the patients; however, no association between HOMA-IR values and BP elevations was detected. Even though nondipping children showed a higher proportion of HOMA-IR alterations, these results were not significant. Other authors have found lower values of HOMA-IR in nondipping children with HTN and obesity [40], leaving open the question of whether or the insulin resistance index and dipping versus nondipping nighttime BPs are related.
This work has several limitations, such as its sample size and the small number of patients with a history of IUGR. Another potential limitation may arise from the study design and the selection of the control group. The best way to accurately determine the potential impact of rhGH treatment on the development of HTN in SGA would be to conduct a randomized clinical trial with a large sample size and more homogeneous samples. On the one hand, considering the positive effects of rhGH in terms of height prognoses in these children, it would be unethical to randomize a group of SGA patients, who fulfilled the criteria, to not receive this treatment. On the other hand, there would be a high risk of bias if we had chosen patients without SGA receiving GH treatment as controls, as their underlying conditions could have interfered with the results. As a control group, instead of selecting only term-born children, we could have included not SGA preterm children as well to better control for the influence of GA. However, we believe that the statistical tests we employed minimized the influence of this on our findings. Finally, our study did not take into account other potential confounders, such as physical activity, dietary habits (including salt intake), or family lifestyle factors, which may influence BP variability.
In conclusion, rhGH treatment does not appear to significantly increase the risk of HTN in children with a history of low birth weight. The subtle alterations described in diastolic BP through ABPM are unlikely to have any clinical impact in the short term; however, they could be a marker to indicate the necessity of closer monitoring from prepubertal stages to detect the future development of HTN. It is particularly relevant to closely monitor those children who are also overweight. Promoting healthy habits like breastfeeding from birth could prevent the onset of HTN in this population. To assess the long-term impact of rhGH therapy in young adults, future research should include prospective studies with ambulatory monitoring and appropriate control groups to detect subtle changes that could predispose to cardiovascular disease.
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