Assessment of body fat mass, anthropometric measurement and cardiometabolic risk in children and adolescents with achondroplasia and hypochondroplasia

Abstract

Achondroplasia is a rare skeletal dysplasia characterized by rhizomelic short stature, whose prevalence is about 1 per 25,000 births. For some patients with achondroplasia, excess body weight is one of the major concerns due to an impaired linear growth. Epidemiological studies revealed a premature onset of cardiovascular or cerebrovascular events in achondroplasia. An association between obesity and cardiometabolic risk factors related to cardiovascular events remains unknown in patients with achondroplasia/hypochondroplasia. This cross-sectional study investigated anthropometric measurements, body compositions and cardiometabolic risk factors in pediatric patients with achondroplasia/hypochondroplasia. Thirty-two patients with achondroplasia and ten with hypochondroplasia aged between 1.9 and 18.7 years were enrolled in this study. Half of the participants presented at least one cardiometabolic abnormality. Elevated systolic blood pressure was the most common abnormality. None of the participants developed metabolic syndrome or type 2 diabetes mellitus. Body mass index-standard deviation score and hip/height ratio were strongly correlated with percent body fat assessed by dual energy X-ray absorptiometry although no significant association was found between anthropometric measurements or body fat mass and any cardiometabolic risk factors. No significant difference in body fat mass, as well as body mass index-standard deviation score and hip/height, was found between cardiometabolically normal group and cardiometabolically abnormal groups. These results suggest that not only weight gain and hip/height changes should be monitored but also individual cardiometabolic risk factors should be evaluated to avoid cardiometabolic events in the healthcare management of pediatric patients with achondroplasia/hypochondroplasia.

ACHONDROPLASIA (ACH) is the most common form of skeletal dysplasia and is characterized by a rhizomelic short stature. It results from a gain-of-function variant in the gene encoding fibroblast growth factor receptor 3 (FGFR3). Patients with ACH present with various systemic complications throughout their life. Obesity is one of the major complications of ACH [1-4]. It currently remains unclear whether patients with hypochondroplasia (HCH), an allelic disease of ACH, are susceptible to obesity [5, 6]. The management of early weight gain in childhood is important because of loading on the joints and lower limbs, which aggravates skeletal deformities and curved lower spines, in addition, obesity worsens sleep apnea, and is a risk factor for cardiovascular events [2, 7-10].

In the general adult population, body mass index (BMI) is used as a principal measurement of obesity and overweight in clinical settings. The BMI standard deviation score (SDS) and BMI percentile have been widely adopted by major guidelines and criteria to evaluate the severity of excess weight and obesity in children and adolescents with an average stature [11, 12]. However, the parameters BMI-SDS and BMI percentile for the general population are possibly unreliable for patients with ACH due to disease-specific disproportions. Previous studies proposed ACH-specific growth and BMI charts [2, 8, 13]. A BMI chart of Japanese children and adolescents with ACH/HCH is not currently available. Percent excess weight (PEW) is adopted in Japan for the assessment of obesity among healthy pediatric populations [14]. In addition to BMI, other indexes such as waist/height, waist/hip and hip/height ratios have been used as parameters associated with visceral adiposity [15-17]. Dual energy X-ray absorptiometry (DXA) is a powerful modality that directly measures fat mass independent of height and weight [18]. Saint-Laurent et al. examined the percent body fat (%BF) in pediatric patients with ACH using DXA and observed that they presented an increasing abdominal fat during their adolescence [19]. To date, no study focusing on the association between %BF and other anthropometric measurements in ACH/HCH has been published.

A long-term survey of morbidity and mortality in ACH revealed a higher prevalence of death associated with cardiovascular and cerebrovascular diseases [7, 20]. The fact that short life expectancy in some patients with ACH results from cardiovascular and cerebrovascular events is thought to be linked to excess weight in young adulthood [19, 21]. A recent study discussing mortality in ACH revealed that the causes of death have changed from sudden death, respiraty infection or hydrocephalus to cardiovascular or cerebrovascular and accidental events [22]. Therefore, a precise assessment of adiposity in ACH is urgently needed for appropriate clinical interventions and management [4]. Studies about the relationship between weight gain and cardiovascular diseases in patients with ACH and HCH have been sparse [7, 21].

The aim of the present study was to find the anthropometric measurements associated with body fat assessed by DXA, the prevalence of cardiometabolic risk factors and an association between body indexes including body fat mass and cardiometabolic risk factors in children and adolescents with ACH and HCH.

Materials and Methods

Editorial policies and ethical considerations

The study protocol was approved by the Institutional Review Board of Osaka University Hospital (No. 15601, No. 700) on February 2017, and the extension of the study was approved on September 2022, and the study was conducted based on the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants or their parents prior to their participation in the present study.

Subjects

We conducted a retrospective, cross-sectional study on patients with ACH/HCH younger than 20 years of age who visited our outpatient clinic at Osaka University Hospital between August 2017 and June 2020. ACH was diagnosed based on a combination of clinical and radiographic features. We excluded patients who were undergoing limb lengthening during the investigation period and those with missing data on %BF but included those who had completed limb lengthening. Therefore, thirty-two patients with ACH and ten patients with HCH were ultimately enrolled in the present study. Twenty-nine children with ACH harbored the c.1138G>A (p.Gly380Arg) pathogenic variant in the FGFR3 gene heterozygously, and two had not undergone genetic testing. HCH was diagnosed by the presence of either the heterozygous c.1620C>G or c.1620C>A (p.Asn540Lys) pathogenic variant in the FGFR3 gene together with clinical and radiographic features.

Measurements

Anthropometric measurements, including height, weight, waist circumference (WC), and hip circumference, were collected from medical records between August 2017 and June 2020. Measurements were conducted following overnight fasting by nurses. WC was measured around the abdomen at the level of the umbilicus, and hip circumference around the hips at the maximum diameter. BMI-SDS, BMI percentile, PEW, as well as waist/height, waist/hip, and hip/height ratios, were calculated. BMI-SDS and BMI percentile were calculated based on the provided reference retrieved from http://jspe.umin.jp/medical/chart_dl.html [23]. This reference ranged from birth to 17.5 years. PEW is the percent of excess weight for standard weight corresponding to height, which is commonly used to assess obesity among the pediatric population with a normal stature in Japan [14].

Definition of metabolic syndrome

The diagnostic criteria for metabolic syndrome (MS) in childhood proposed by the Ministry of Health, Labour and Welfare of Japan were used in the present study [24]. These criteria were modified based on pediatric and adolescent MS criteria addressed by the International Diabetes Federation. MS was defined as fulfilling WC criteria: (a) each individual meets WC >75 cm in children aged 6 to 11 years and WC >80 cm in adolescents aged 12 to 15 years, or waist/height is greater than 0.5; and any two of the following three factors: (b) serum triglycerides (TG) >1.35 mmol/L (120 mg/dL) or high-density-lipoprotein cholesterol (HDL-C) <1.03 mmol/L (40 mg/dL); (c) systolic blood pressure (sBP) >125 mmHg and/or diastolic blood pressure (dBP) >70 mmHg; and (d) fasting plasma glucose (FPG) >5.6 mmol/L (100 mg/dL).

Data collection

Percent whole body fat (%whole BF), percent fat mass (%FM) and percent lean body mass (%LBM) were measured based on DXA scans using a Hologic Discovery A device (Hologic Inc., MA, USA) to quantify body fat mass and lean body mass. The term of %whole BF in this study indicates the percent of the whole-body fat mass divided by the whole-body mass except for the head. Trunk %FM and trunk %LBM indicate the percent of trunk fat mass divided by total fat mass and the percent of trunk lean body mass divided by total lean body mass, respectively. BP was measured once in a seated position at rest with using an electronic sphygmomanometer (ES-H55P; Terumo, Tokyo, Japan). In participants who had difficulties staying in a sitting position, we measured BP in the supine position. To deal with the difficulty of precise measurements of BP due to the characteristic short rhizomelic arms, we measured BP with adjusted cuff sizes to properly cover their arm length and width for each patient. When BP was higher than the upper-normal level in the first measurement, a second measurement was performed. Hypertension was defined as higher than or equal to the 95^th percentile of standardized BP by sex and age [25]. Regarding factors associated with MS and cardiometabolic risk factors, FPG, fasting insulin, HDL, low-density lipoprotein cholesterol (LDL), and TG levels were measured in blood collected from every patient via venipuncture after an overnight fast. LDL, HDL, and TG levels were measured using BioMajesty^TM JCA-BM8040 (JEOL Ltd. Tokyo, Japan), whereas glucose and insulin levels were assessed using LUMIPULSE L2400 (FUJIREBIO, Tokyo, Japan) and a LUMIPULSE presto insulin kit, respectively. Homeostasis model assessment insulin resistance (HOMA-IR) was calculated using the following formula: fasting insulin (μIU/mL) × FPG (mg/dL)/405. Abnormal values in FPG, TG, and HDL were defined as higher than 5.6 mmol/L, 1.35 mmol/L, and lower than 1.03 mmol/L, respectively. Hyperinsulinemia was defined as a fasting insulin level greater than 108 pmol/L [26]. Insulin resistance (IR) was defined as HOMA-IR greater than 2.5 [27]. Anthropometric measurements, BP, and laboratory data were obtained within a period of one month before or after a DXA scan.

Statistical analysis

Continuous variables are expressed as medians [minimum–maximum]. Differences in continuous or nominal variables were analyzed by the Mann-Whitney U test or chi-squared test, respectively. Correlation between variables were assessed using Pearson’s correlation test. Multiple regression analysis was performed to analyze differences between variables adjusted for GH treatment. The significance of differences was defined as p < 0.05. All statistical analyses were performed using either JMP pro 15.1.0 software (SAS Institute, Cary, NC, USA) or IBM SPSS for Mac version 23 Statistics software (SPSS, Inc., Chicago, IL, USA).

Results

Patient demographics

Forty-two subjects with ACH and HCH, consisting of 24 males (57.1%) and 18 females, were examined (Table 1). Median ages were 9.8 years (1.9–18.5) in males with ACH and HCH, and 11.7 years (range 3.1–18.7) in females with ACH and HCH. Thirty-one children (73.8%) received GH therapy (0.35 mg/kg/week) at the time of the evaluation; one male with HCH had never received GH therapy, and ten had completed GH therapy before the initiation of the investigation. The median time from the discontinuation of the therapy to the investigation was 2.9 years (ranged from 0.5–3.3). The prevalence of participants whose BMI-SDS was beyond 2SD was 30.4% in males and 22.2% in females. The ratio of participants with a BMI above the 95^th percentile was 43.5% in males (10 out of 23) and 27.8% in females (5 out of 18).

Table 1 Demographics of patients with ACH and HCH at baseline

	Males with ACH	Males with HCH	p value	Females with ACH	Females with HCH	p value
Age (yr)	9.5 [1.9–18.5] (19)	11.4 [4.5–16.4] (5)	0.17	12.0 [3.1–18.7] (13)	11.5 [9.8–18.3] (5)	0.46
GH duration (yr)	8.3 [2.1–13.6] (19)	4.7 [0–12.5] (5)	>0.99	10.6 [2.2–11.7] (13)	10.3 [9.6–10.5] (5)	0.81
%whole BF	23.0 [12.5–52.1] (19)	17.0 [13.5–19.2] (5)	0.03	25.7 [19.0–48.4] (13)	27.6 [18.7–33.1] (5)	0.73
%whole LBM	75.0 [46.0–86.0] (19)	79.0 [78.0–84.0] (5)	0.04	72.0 [49.0–78.0] (13)	70.0 [64.0–79.0] (5)	0.69
%trunk FM	42.8 [31.4–47.1] (19)	38.6 [27.7–46.3] (5)	0.27	41.5 [37.0–48.3] (13)	37.0 [28.3–41.7] (5)	0.02
%trunk LBM	60.9 [56.0–63.9] (19)	54.6 [52.5–57.0] (5)	0.002	60.4 [55.3–66.0] (13)	56.8 [42.5–58.2] (5)	0.03
%limb FM	57.2 [52.9–68.5] (19)	61.4 [53.7–72.3] (5)	0.27	58.5 [51.7–63.0] (13)	62.2 [51.2–65.9] (5)	0.30
%limb LBM	39.2 [36.0–44.0] (19)	45.4 [45.0–48.0] (5)	0.002	39.6 [34.0–45.0] (13)	43.2 [42.0–45.0] (5)	0.03
BMI-SDS (SD)	1.63 [0.08–2.88] (18)	0.52 [–0.25–3.03] (5)	0.23	1.61 [0.48–6.08] (13)	1.44 [1.03–1.58] (5)	0.35
BMI percentile	94.8 [53.0–99.8] (18)	70.0 [40.1–99.9] (5)	0.23	94.6 [68.6–100.0] (13)	92.5 [84.9–94.3] (5)	0.35
Waist/height	0.51 [0.45–0.68] (14)	0.45 [0.43–0.59] (5)	0.12	0.52 [0.48–0.71] (13)	0.50 [0.47–0.52] (5)	0.07
Waist/hip	0.77 [0.70–0.91] (14)	0.78 [0.75–0.98] (5)	0.31	0.78 [0.69–0.91] (13)	0.74 [0.68–0.79] (5)	0.26
Hip/height	0.66 [0.60–0.92] (14)	0.58 [0.57–0.60] (5)	0.002	0.66 [0.62–0.78] (13)	0.66 [0.64–0.69] (5)	0.73
PEW (%)	72.2 [12.6–324.2] (14)	24.8 [–3.5–34.5] (5)	0.004	59.6 [5.9–145.7] (11)	34.3 [18.8–50.8] (4)	0.12
FPG (mmol/L)	5.0 [3.1–5.9] (17)	5.1 [4.8–5.7] (5)	0.29	5.1 [4.8–6.1] (12)	5.1 [5.1–5.4] (5)	0.52
FPG (mg/dL)	90 [55–106] (17)	92 [86–102] (5)	0.25	91 [86–109] (12)	92 [91–97] (5)	0.52
HbA1c (mmol/mol)	35.5 [29.0–39.9] (18)	34.4 [31.2–37.7] (5)	0.71	35.5 [32.2–36.6] (12)	35.5 [34.4–36.6] (5)	0.74
HbA1c (%)	5.4 [4.8–5.8] (18)	5.3 [5.0–5.6] (5)	0.71	5.4 [5.1–5.5] (12)	5.4 [5.3–5.5] (5)	0.74
Insulin (pmol/L)	37.3 [13–139] (17)	56.0 [37–153] (5)	0.11	50.6 [33–121] (12)	43 [28–68] (5)	0.11
Insulin (μIU/mL)	5.2 [1.81–19.4] (17)	7.8 [5.2–21.3] (5)	0.11	7.1 [4.6–16.9] (12)	6.0 [3.95–9.6] (5)	0.11
HOMA-IR	1.08 [0.39–4.26] (17)	1.84 [1.16–5.36] (5)	0.08	1.69 [1.02–3.88] (12)	1.35 [0.89–2.3] (5)	0.17
HDL (mmol/L)	1.51 [1.11–1.97] (16)	1.52 [1.11–1.66] (5)	0.41	1.51 [1.22–1.94] (12)	1.37 [1.03–1.86] (5)	0.37
HDL (mg/dL)	58.5 [43–76] (16)	59 [43–64] (5)	0.41	58.5 [47–75] (12)	53 [40–72] (5)	0.37
LDL (mmol/L)	2.48 [1.6–3.4] (15)	2.35 [1.4–2.9] (5)	0.66	2.73 [1.4–3.9] (12)	3.1 [1.8–3.4] (5)	0.75
LDL (mg/dL)	96 [63–133] (15)	91 [55–114] (5)	0.66	105.5 [54–149] (12)	120 [71–130] (5)	0.75
TG (mmol/L)	0.47 [0.26–1.66] (17)	0.77 [0.63–1.89] (5)	0.05	0.82 [0.43–1.45] (12)	0.79 [0.41–1.05] (4)	0.63
TG (mg/dL)	42 [23–1.47] (17)	68 [56–167] (5)	0.05	73 [38–128] (12)	70 [36–93] (4)	0.63
sBP (mmHg)	110 [91–139] (19)	96 [95–108] (5)	0.027	114 [80–134] (12)	105 [84–117] (5)	0.27
dBP (mmHg)	64 [48–74] (19)	54 [48–71] (5)	0.46	67.5 [47–87] (12)	66 [55–75] (5)	0.83

Numbers are presented as medians [minimum–maximum]; numbers in parentheses indicate the number of subjects. Significant values are in bold. ACH indicates achondroplasia, HCH: hypochondroplasia, BMI: body mass index, SDS: standard deviation score, PEW: percentage of excess weight, %BF: percent of body fat, %trunk FM: percent of fat mass. %trunk LBM: percent of lean body mass, FPG: fasting plasma glucose, HOMA-IR: homeostasis model assessment of insulin resistance, HDL: high density of lipoprotein, LDL: low density of lipoprotein, TG: triglyceride, sBP: systolic blood pressure, dBP: diastolic blood pressure.

Relationships between body composition and anthropometric measurements

In male, %whole BF, hip/height and PEW were significantly greater in participants with ACH than in those with HCH, whereas %whole LBM was greater in individuals with HCH than those with ACH (Table 1). Female patients with HCH had less %trunk FM than those with ACH. Patients with ACH had a significantly higher %trunk LBM and lower limb LBM than those with HCH in both sexes (Table 1). The distribution of each index is shown as scatter plots (Supplementary Fig. 1). Significant correlation with %whole BF was observed in BMI-SDS (r = 0.429, p = 0.041), waist/height (r = 0.728, p < 0.0001), hip/height (r = 0.886, p < 0.0001), and PEW (r = 0.469, p = 0.043) in males with ACH and HCH, and in BMI-SDS (r = 0.632, p = 0.005) and hip/height (r = 0.777, p < 0.0001) in females (Fig. 1 and Supplementary Fig. 2).

Fig. 1

Scatter plots showing the correlation between %whole BF and each index. Figures on the left side represent male patients and those on right side female patients. The numbers of male patients are 23 in BMI-SDS and 19 each in hip/height, waist/height, and PEW. The numbers of female patients are 18 each in BMI-SDS, and hip/height. Filled circles indicate achondroplasia, and crosses indicate hypochondroplasia.

%whole BF indicates whole-body fat percent, BMI-SDS: body mass index-standard deviation score, PEW: percent excess weight.

Examination of cardiometabolic risk factors

Values of cardiometabolic risk factors and their frequency are shown in Tables 1–3. The prevalence of patients with cardiometabolic abnormalities was significantly higher in ACH than HCH patients in the female, but not in the male group (Tables 2, 3). In the ACH group, sBP was elevated in all age groups (Table 2), which was the most common abnormality; 41.7% of males and 35.3% of females met the criteria of hypertension adjusted for age and sex. In the HCH group, there is only one patient without GH therapy, and he had hyperglycemia and hyperinsulinemia. Half of the participants exhibited at least one cardiometabolic abnormality. Five participants presented two cardiometabolic abnormalities, and two females presented three cardiometabolic abnormalities. None of the patients showed abnormalities in glycated hemoglobin (HbA1c) and HDL levels. None of the patients fulfilled the criteria of either MS or type 2 diabetes mellitus. A family history of hypertension or diabetes was found in 26.2% (11 out of 42). Five families had a family history of both hypertension and diabetes.

Table 2 Prevalence of cardiometabolic abnormalities in male participants with ACH and HCH

Age (yr)	Males with ACH			Males with HCH			p value
	Age <7	7–12yr	Age >12	Age <7	7–12yr	Age >12
Cardiometabolic abnormality	100% (6/6)	30% (3/10)	33.3% (1/3)	N/A	50% (1/2)	50% (1/2)	>0.99
FPG >100 mg/dL	25% (1/4)	0% (0/10)	0% (0/3)	N/A	0% (0/2)	50% (1/2)	0.41
Insulin >15 μIU/mL	0% (0/4)	10% (1/10)	0% (0/3)	N/A	0% (0/2)	50% (1/2)	0.41
HOMA-IR >2.5	0% (0/4)	20% (2/10)	0% (0/3)	N/A	0% (0/2)	50% (1/2)	>0.99
High sBP	100% (6/6)	20% (2/10)	33.3% (1/3)	N/A	0% (0/2)	0% (0/2)	0.36
High dBP	16.7% (1/6)	0% (0/10)	0% (0/3)	N/A	0% (0/2)	0% (0/2)	0.38
LDL >140 mg/dL	0% (0/3)	0% (0/9)	0% (0/3)	N/A	0% (0/2)	0% (0/2)	ND
TG >120 mg/dL	0% (0/4)	10% (1/10)	0% (0/3)	N/A	50% (1/2)	0% (0/2)	0.41

Numbers in parenthesis indicate the number of subjects with abnormal values out of total subjects measured. ACH: achondroplasia, HCH: hypochondroplasia, FPG: fasting plasma glucose, HOMA-IR: homeostasis model assessment of insulin resistance, sBP: systolic blood pressure, dBP: diastolic blood pressure, LDL: low density of lipoprotein, TG: triglyceride, N/A: not applicable, ND: not datamined. sBP and dBP indicate ones whose systolic BP was higher than or equal to the 95^th percentile of standardized BP by sex and age.

Table 3 Prevalence of cardiometabolic abnormalities in female participants with ACH and HCH

Age (yr)	Females with ACH			Females with HCH			p value
	Age <7	7–12yr	Age >12	Age <7	7–12yr	Age >12
Cardiometabolic abnormality	66.7% (2/3)	50% (2/4)	66.7% (4/6)	N/A	0% (0/3)	0% (0/2)	0.036
FPG >100 mg/dL	50% (1/2)	0% (0/4)	0% (0/6)	N/A	0% (0/3)	0% (0/2)	>0.99
Insulin >15 μIU/mL	0% (0/2)	0% (0/4)	20% (1/5)	N/A	0% (0/3)	0% (0/2)	>0.99
HOMA-IR >2.5	0% (0/2)	0% (0/4)	16.7% (1/6)	N/A	0% (0/3)	0% (0/2)	>0.99
High sBP	50% (1/2)	50% (2/4)	50% (3/6)	N/A	0% (0/3)	0% (0/2)	0.10
High dBP	0% (0/2)	0% (0/4)	33.3% (2/6)	N/A	0% (0/3)	0% (0/2)	>0.99
LDL >140 mg/dL	50% (1/2)	0% (0/4)	33.3% (2/6)	N/A	0% (0/3)	0% (0/2)	0.52
TG >120 mg/dL	0% (0/2)	0% (0/4)	16.7% (1/6)	N/A	0% (0/3)	N/A	>0.99

Numbers in parenthesis indicate the number of subjects with abnormal values out of total subjects measured. Significant values are in bold. ACH: achondroplasia, HCH: hypochondroplasia, FPG: fasting plasma glucose, HOMA-IR: homeostasis model assessment of insulin resistance, sBP: systolic blood pressure, dBP: diastolic blood pressure, LDL: low density of lipoprotein, TG: triglyceride, N/A: not applicable. sBP and dBP indicate ones whose systolic BP was higher than or equal to the 95^th percentile of standardized BP by sex and age.

When we investigated the association between %whole BF, %trunk FM or %limb FM, as well as anthropometric measurements and each cardiometabolic risk factor, cardiometabolic risk factors were neither associated with %whole BF adjusted for sex and age, %trunk FM or %limb FM nor with BMI-SDS for sex and age and hip/height (Supplementary Tables 1–8). Next, we categorized participants into two groups; a group in which participants presented no cardiometabolic abnormality (cardiometabolically normal group) and another group in which participants presented one or more cardiometabolic abnormality (cardiometabolically abnormal group). The cardiometabolically abnormal group consisted of 13 out of 24 males (54.2%) and 8 out of 18 females (44.4%). We examined whether %whole BF, %trunk FM, %limb FM, BMI-SDS or hip/height ratio were different between two groups. None of these parameters were significantly different (Supplementary Tables 9, 10). Moreover, these results did not change even when adjusted for GH therapy (Supplementary Tables 11, 12). We also investigated the association between cardiometabolic risk factors and family history but did not find any significant association (Supplementary Table 13).

Lastly, we investigated the impact of GH therapy on metabolic status. No significant differences in FPG, HbA1c, insulin and HOMA-IR were found between untreated and treated patients (Supplementary Tables 14, 15). Likewise, no significant difference regarding hyperinsulinemia or insulin resistance was detected between hypertensive and normotensive patients (Supplementary Table 16). However, elevated FPG, hyperinsulinemia and abnormal HOMA-IR were observed in the patient who had never received GH therapy.

Discussion

The results of this cross-sectional study showed a descriptive analysis and the association between body composition and anthropometric measurements in ACH and HCH. The study described in detail the status of cardiometabolic abnormalities in children and adolescents with ACH or HCH. The ACH and HCH groups differed regarding the ratio of trunk FM to limb FM. Moreover, patients with ACH tended to have a greater %LBM in the trunk, whereas those with HCH had a greater %LBM in the limbs. To the best of our knowledge, these differences have not been reported yet. We speculate that these results reflect differences in the severity of limb shortening between patients with ACH and those with HCH.

Hip/height was strongly correlated with %BF. Despite of high %BF, as well as high BMI-SDS and BMI percentiles, none of the participants developed MS or type 2 diabetes mellitus. The usefulness of hip/height as an anthropometric index is controversial. A previous study suggested that hip/height was a potential body index reflecting body adiposity in some populations [15]. In an obese Japanese pediatric study population, hip/height was found to be associated with adiposity [16]. By contrast, other studies showed that waist/height and waist/hip were better indexes associated with cardiometabolic risk factors among the general population [28-34]. These reported different outcomes suggest that conventional anthropometric data are of limited use for precisely predicting the quantity of the fat mass in population with ACH. The current study suggests that hip/height, as well as BMI-SDS and waist/height, may be useful in the assessment of adiposity in patients with ACH and HCH. The small scale of the current study and its ethnic restriction may limit the adaptation of our findings to patients with ACH and HCH at larger scales. Moreover, a sex difference was observed in the correlation between %whole BF and waist/height, which might suggest preferentially abdominal fat masses in males.

The present study indicated that an elevated sBP was the most common abnormality among cardiometabolic risk factors (41.7% for sBP in males, 35.3% for sBP in females) (Tables 2, 3). A recent study from Norway showed that the prevalence of hypertension in adults with ACH was 52% in males and 14% in females [35]. Another study from the U.S. showed that the prevalence of hypertension in adults with ACH was 55.7% in males and 35.2% in females [36]. Our results were consistent with these current studies [36]. The trend of a high prevalence of hypertension in the youngest age group in males is an interesting result. We speculate this might suggest that younger male patients with ACH are susceptible to having a high sBP, which requires further investigation.

None of the participants fulfilled the criteria for MS nor type 2 diabetes. Saint-Laurent et al. reported that children with ACH developed an increase in abdominal fat mass during adolescence along with less metabolic changes by showing the normality in FPG and insulin levels [37], suggesting that children with ACH were unlikely to present changes in glucose metabolism. Whether patients with ACH and HCH are predisposed to hyperglycemia or hyperinsulinemia in childhood remains unknown. Although in the current study, the mean FPG level (5.0 ± 0.6 mmol/L), insulin level (7.5 ± 4.2 μIU/mL) and HOMA-IR (1.69 ± 1.03) were within the normal range, seven out of forty-two participants presented some abnormalities in either FPG level, insulin level, or HOMA-IR. Two previous studies showed that children and adolescents with ACH/HCH were normal in either FPG (4.5 ± 0.7 and 4.9 ± 0.5 mmol/L), insulin (6.1 ± 4.5 and 5.32 ± 1.9 μIU/mL), or HOMA-IR (1.17 ± 0.8 and 1.14 ± 0.5, respectively) [38, 39] which is comparable with our data. In contrast, two other studies reported that boys with ACH presented hyperinsulinemia and insulin resistance [40, 41]. Moreover, another study showed that some children and adolescents with ACH showed prolonged hyperglycemia in the oral glucose tolerance test [42]. Collectively, our results together with previous reports suggest that no unified view has been obtained to date on the effects of hyperinsulinemia and impaired glucose tolerance on patients with ACH and HCH.

No significant associations of %whole FM, %trunk FM, and %limb FM with cardiometabolic abnormalities were observed (Supplementary Tables 1–8). Likewise, no significant difference in %whole FM (including %trunk FM, and %limb FM), BMI-SDS, and hip/height was found between the cardiometabolically normal group and cardiometabolically abnormal groups (Supplementary Tables 9, 10). Furthermore, no significant differences in insulin level and HOMA-IR were found between the normotensive and hypertensive groups (Supplementary Table 16). These results may have been observed because the participants were children and adolescents. The exacerbation in cardiometabolic status in childhood is not always dependent on increasing body weight [43]. We assume that those who presented any cardiometabolic abnormalities in childhood or adolescence could be predisposed to MS or type 2 diabetes later in life.

The present study has several limitations. First, GH therapy is thought to lead to hyperglycemia and insulin resistance, and the majority of participants (31 out of 42) had a history of treatment with high doses of GH in our cohort. As shown in Supplementary Tables 4, 5, the present results revealed no significant differences in FPG, HbA1c, insulin levels, or HOMA-IR between patients treated with high doses of exogenous GH and untreated patients. GH treatment for ACH and HCH is unique to Japan, and its associated studies have been limited. In a five-year study, insulin levels were not elevated in 35 children with ACH treated with GH [44]. Long-term effects of GH treatment on glucose and lipid metabolism, as well as body composition, in children and adolescents with ACH and HCH have not been established to date. Second, we did not measure abdominal or hip fat using peripheral quantitative computed tomography. Previous studies indicated that the abdominal waist-to-hip fat ratio in %BF, which reflects WC and waist/hip, is associated with MS and cardiovascular diseases [18, 33, 45]. However, high radiation exposure was not considered to be ethical for children and adolescents in the present study. Third, we did not examine other risk factors that may have affected obesity and hyperinsulinemia, including eating habits, physical activity, the longitudinal weight changes, and durations of excess weight. However, this study showed correlations of BMI-SDS and hip/height with %whole BF measured by DXA, as well as a high incidence of hypertension and at least one of cardiometabolic abnormality in half of the participants with ACH and HCH. These findings may provide insights into the healthcare management of pediatric patients with ACH and HCH.

In conclusion, our study finding that half of the participants with ACH and HCH in childhood and adolescence present at least one of the cardiometabolic abnormalities in our study suggests the significance of not only managing weight changes but also monitoring individual cardiometabolic risk factors since childhood for early medical intervention.

Acknowledgements

We thank the patients and their families for their participation in the present study. This work was supported by grants from the Research Program on Rare and Intractable Diseases of Health, Labour and Welfare Sciences Research Grants of Japan, H28-Nanchito (Nan)-Ippann-017 and 19FC1006 (Principal Investigator, Hideaki Sawai) and 22FC1012 (Principal Investigator, Takuo Kubota) and from JSPS KAKENHI Grant Number JP18K07877 (Principal investigator, Taichi Kitaoka). The authors would like to thank Wiley Editing Services (https://wileyeditingservices.com/en/) for English language editing.

Disclosures

K.O. received lecture fees from Biomarin Pharma Inc., Kyowa Kirin Co., Ltd., and Novo Nordisk Pharma Ltd. T. Ku. received a research grant from Novo Nordisk Pharma Ltd. All other authors have nothing to declare.

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