The relationship between different metabolic phenotypes and bone indices in Chinese children and adolescents aged 12–18 years old

Abstract

Data regarding different metabolic phenotypes and bone markers including bone mineral content (BMC) and osteocalcin (OCN) among children and adolescents are very limited. Hence, the purpose of this investigation was to explore the relationship between different metabolic phenotypes and BMC or OCN among Chinese children and adolescents. This cross-sectional study included 1,328 children and adolescents aged between 12 and 18 years who were selected from four schools in Yinchuan city from 2018 to 2020 by stratified cluster random sampling. Subjects were divided into four groups according to BMI and metabolic status, as follows: metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), metabolically unhealthy normal weight (MUNW), and metabolically healthy normal weight (MHNW). The MHNW, MUNW, MHO, and MUO phenotypes in boys were 48.4%, 30.5%, 6.7%, and 14.4%, respectively, and were 47.8%, 33.6%, 6.6%, and 12.1% in girls, respectively. The MHO and MUO phenotypes had higher BMC than the MHNW or MUNW phenotype (all p < 0.05), and the MUO phenotype with BMC was significantly higher than MHO group in boys (p < 0.05). We discovered a significant positive correlation between BMC and the MHO (OR = 8.82, 95% CI = 2.04–38.16), MUO phenotypes (OR = 13.53, 95% CI = 4.10–44.70), while no association was found between OCN and metabolic phenotypes in neither boys nor girls. Overweight/obese children and adolescents had higher BMC, and there existed sex differences in the effect of metabolic status on BMC among them. OCN was not supposed to be an index of bone health in this study.

THE LAST TWO DECADES have seen a growing trend towards obesity prevalence in children and adolescents, which has become an important public health problem worldwide. More than 340 million children and adolescents aged 5 to 19 are overweight or obese globally. This has become a coexisting risk factor for the treatment of metabolic disorders, such as hyperglycaemia, hypertension, dyslipidaemia and cardiovascular disease, and is also associated with some types of cancer and increased mortality [1-3].

However, metabolically healthy individuals without any type of metabolic abnormality are also found in obese people, termed the metabolically healthy obesity (MHO) phenotype [4, 5]. In addition, individuals with metabolic abnormalities, such as hyperglycaemia, and high TG, are also found in normal weight populations, known as the metabolically unhealthy normal weight (MUNW) phenotype [6, 7]. Therefore, based on human body mass index (BMI) and metabolic health, subjects can be divided into four groups: MHO, metabolically unhealthy obesity (MUO), MUNW, and metabolically healthy normal weight (MHNW) [8].

Childhood and adolescence are critical periods of growth and development, and any disease or condition that reduces bone mineral accumulation during this process may lead to peak bone mass loss, which increases the risk of future fractures and osteoporosis [9]. Overweight children and adolescents have higher bone mass than normal children [10], which can reduce the risk of osteoporotic fractures [11]. The obesity paradox refers to this protective effect of obesity, which may be related to metabolic abnormalities [12]. Hetherington-Rauth et al. found that body fat mass in each group was significantly positively correlated with bone mineral content (BMC) when girls were stratified by cardiometabolic risk factors (CMRs), while girls with CMRs ≥2 had a lower BMC at a certain proportion of body fat [12]. It has previously been observed that metabolically unhealthy obese or normal weight individuals were found to have a higher risk of low bone quality among elderly population [13]. In contrast, the total bone mineral density of the MUHO phenotype is higher that of the than MHO phenotype, as demonstrated by an adult study [14]. However, data regarding BMC and metabolic phenotypes in children and adolescents are scarce. Therefore, more studies are needed to confirm the impact of different metabolic phenotypes on BMC particularly in children and adolescents.

A common indicator of bone formation is osteocalcin (OCN), a protein produced by osteoblasts that is dependent on vitamin K [15]. It can reduce visceral fat mass, increase energy expenditure, and improve insulin sensitivity by enhancing the number of pancreatic β-cells and insulin secretion [16]. However, the relationship between different metabolic phenotypes and markers of bone formation among adolescents is not fully known. Hence, the purpose of this investigation was to explore the relationship between different metabolic phenotypes and BMC or OCN among Chinese children and adolescents.

Methods

Study population

Only children and adolescents aged between 12 and 18 years were included in this cross-sectional study. Participants were selected from junior middle schools and senior high schools in Yinchuan city from 2018 to 2020 by stratified cluster random sampling. First, we chose four schools by a convenience sampling method, and then stratified them according to grades. Finally, 39 classes were randomly clustered from each grade, and 1,438 subjects were selected. All subjects completed a questionnaire survey, physical examination, body composition measurement and laboratory tests. In total, 1,328 subjects were obtained for the analysis after excluding individuals with physical impairments; hyperthyroidism; blood system diseases; severe bone and joint diseases; long-term use of glucocorticoids, antiepileptic and other drugs affecting bone metabolism; metabolic-related diseases; and missing data. Informed consent was signed by all participants and their parents/guardians. The study was conducted according to the standards of the Declaration of Helsinki and approved by the Ethics Committee of Ningxia Medical University (2021-G053).

Questionnaire investigation

General demographic characteristics, including name; age; gender; and information about diet, sleep condition, and physical activity (PA), were obtained from the questionnaire. The sleep time was determined by the time sleeping at night and getting up in the morning during school. PA was assessed by the participants reporting the frequency of their leisure-time PA, which resulted in heavy perspiration, significant increases in respiration or heart rate, and lasted at least 30 minutes in the previous week.

Anthropometrical data

According to the measurement standards of each instrument, data for the study were collected by trained staff at each chosen school. A mechanical stadiometer (ZH7082) and an electronic scale (RGT-140) were used to measure the subjects’ height and weight. Nylon tape was used to measure waist circumference (WC). They were all measured twice to an accuracy of 0.1 cm and 0.1 kg for height/WC and weight, respectively, and the values were averaged for inclusion in the final analysis. The “2014 National Student Physical Fitness and Health Survey Manual” requirements were followed when measuring the subjects’ height, weight, and WC [17]. Weight divided by height squared (kg/m²) was used to calculate BMI. A calibrated electronic sphygmomanometer was used to measure blood pressure (BP) (OMRON HEM-7012, Omron Healthcare, Kyoto, Japan) according to the standard method by the “American Hypertension Education Project Working Group” [18]. Systolic BP (SBP) and diastolic BP (DBP) were measured three times at 1-minute intervals, and the average of the last two readings were used for the final analysis (a third measurement was made if the difference between the first two blood pressure values exceeded 10 mmHg) (1 mmHg = 0.133 kPa). A bioelectrical impedance analyzer was used to test the subjects for BMC (InBody-370, Biospace of Korea, Seoul, Korea).

Biochemical measurements

When the subjects were fasting in the morning, 3 mL of cubital venous blood was drawn into the procoagulant tube, and serum was collected after centrifugation at 3,000 r/min for 10 min (TDL-5-A, centrifugal radius 17.9 cm). Then, the samples were placed in a –80°C freezer for later use. Fasting blood glucose (FBG), total cholesterol (TC), and triacylglycerol (TG) levels were determined by enzymatic methods. High-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were detected by the catalase method. The above indicators were all detected using an automatic biochemical analyser (American AU480). Osteocalcin and insulin were detected by using a human osteocalcin (OCN/BGP) and insulin (INS) enzyme-linked immunosorbent assay kit, respectively, and the data were recorded on a full-wavelength multifunction microplate reader (Multiskan GO type).

Definitions

Overweight and obesity were determined according to the Chinese school-aged children and adolescents screening criteria for overweight and obesity (WS/T-586-2018) with sex-specific and age-specific BMI cut-offs [19]. The MHO was determined according to the consensus proposed by Damanhoury et al. [20], including HDL-C >40 mg/dL (or >1.03 mmol/L), TG ≤150 mg/dL (or ≤1.7 mmol/L), systolic and diastolic blood pressure ≤90th percentile, and FBG ≤100 mg/dL (or ≤5.6 mmol/L). Study subjects were divided into four groups based on BMI and metabolic status, as follows: metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), metabolically unhealthy normal weight (MUNW), and metabolically healthy normal weight (MHNW).

Statistical analysis

Epidata 3.1 software was used to input the data, and all analyses were carried out using SPSS for Windows version 25.0 (IBM Co., Chicago, IL, USA). Normally or nonnormally distributed variables were described by the means ± standard deviation (SD) or medians (25th–75th percentiles) respectively, and categorical variables were calculated by the frequencies and percentages. Comparisons between the differences of continuous variables in different phenotypes were made using variance analysis or the Kruskal-Wallis H test. According to the 50th percentile, OCN and BMC were divided into high and low levels. A binary logistic regression analysis was performed using OCN and BMC levels as dependent variables and different phenotypes as the independent variable. The correlation between BMC and cardiovascular risk factors was analysed by partial correlation. A two-sided p < 0.05 was considered statistically significant.

Results

Tables 1 and 2 present the clinical and anthropometric characteristics of the study population among different metabolic phenotypes by sex. The MHNW, MUNW, MHO, and MUO phenotypes were 48.4%, 30.5%, 6.7%, and 14.4% in boys, respectively, and 47.8%, 33.6%, 6.6%, and 12.1% in girls. There was a statistically significant difference between the four groups in terms of weight z score, height z score, BMI z score, WC z score, SBP, DBP, FBG, and TG both in boys and girls and LDL-C and HDL-C in boys. Moreover, in comparison with MHNW participants, MUNW individuals had a higher weight z score, height z score, WC z score, SBP, DBP, FBG, and TG in boys (all p < 0.05). Among girls, only SBP, DBP, FBG and physical activity were significantly higher in the MUNW group than in the MHNW group (p < 0.05). Moreover, MHO subjects showed more favorable weight z scores, BMI z scores, WC z scores, SBP, DBP and TG than MUO participants among boys and girls (all p < 0.05).

Table 1 Characteristics of boys among different metabolic phenotypes (n = 855)

	MHNW	MUNW	MHO	MUO	p value#
n (%)	414 (48.4)	261 (30.5)	57 (6.7)	123 (14.4)
Age, years	15.14 ± 1.44	15.38 ± 1.42	14.35 ± 1.67	14.63 ± 1.48	<0.001
Weight z score	–0.45 ± 0.56	–0.29 ± 0.64a	1.23 ± 0.74	1.56 ± 0.87b	<0.001
Height z score	–0.18 ± 0.99	0.03 ± 0.99a	0.25 ± 0.83	0.41 ± 0.96	<0.001
BMI z score	–0.45 ± 0.50	–0.35 ± 0.57	1.35 ± 0.81	1.63 ± 0.81b	<0.001
WC z score	–0.47 ± 0.44	–0.31 ± 0.55a	1.28 ± 0.85	1.65 ± 0.92b	<0.001
SBP (mmHg)	107.83 ± 8.90	116.34 ± 11.60a	114.26 ± 7.92	124.25 ± 10.11b	<0.001
DBP (mmHg)	64.02 ± 6.85	69.76 ± 8.70a	67.26 ± 5.32	72.32 ± 8.06b	<0.001
FBG (mmol/L)	4.52 ± 0.51	5.25 ± 0.98a	4.66 ± 0.50	4.95 ± 0.73	<0.001
TC (mmol/L)	3.89 ± 0.83	3.97 ± 1.02	3.99 ± 0.77	4.14 ± 1.21	0.067
TG* (mmol/L)	0.81 (0.65, 1.01)	0.93 (0.73, 1.19)a	0.93 (0.77, 1.30)	1.22 (0.91, 1.72)b	<0.001
LDL-C (mmol/L)	2.13 ± 0.72	2.16 ± 0.79	2.34 ± 0.72	2.48 ± 1.07	<0.001
HDL-C (mmol/L)	1.48 ± 0.31	1.43 ± 0.46	1.30 ± 0.20	1.20 ± 0.34	<0.001
Sleep time (hours)	7.78 ± 0.93	7.62 ± 0.92	7.75 ± 0.95	7.57 ± 0.91	0.060
Physical activity (%)	284 (68.6)	192 (73.6)	39 (68.4)	75 (61.0)	0.099

MHNW, Metabolically healthy normal weight; MUNW, Metabolically unhealthy normal weight; MHO, Metabolically healthy obesity; MUO, Metabolically unhealthy obesity; BMI, Body mass index; WC, Waist circumference; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; FBG, Fasting blood glucose; TC, Total cholesterol; TG, Triacylglycerol; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol.

* Variables are not normally distributed. ^# p for ANOVA tests or Kruskal-Wallis H test.

^a Difference between MUNW vs. MHNW.

^b Difference between MUO vs. MHO.

Table 2 Characteristics of girls among different metabolic phenotypes (n = 473)

	MHNW	MUNW	MHO	MUO	p value#
n (%)	226 (47.8)	159 (33.6)	31 (6.6)	57 (12.1)
Age, years	14.54 ± 1.60	14.75 ± 1.66	14.42 ± 1.65	14.33 ± 1.69	0.313
Weight z score	–0.35 ± 0.61	–0.36 ± 0.62	1.22 ± 0.68	1.71 ± 0.92b	<0.001
Height z score	0.01 ± 0.96	–0.13 ± 1.01	0.01 ± 0.91	0.32 ± 1.06	0.039
BMI z score	–0.40 ± 0.56	–0.35 ± 0.60	1.39 ± 0.61	1.79 ± 0.74b	<0.001
WC z score	–0.35 ± 0.60	–0.37 ± 0.55	1.25 ± 0.73	1.75 ± 0.93b	<0.001
SBP (mmHg)	103.70 ± 7.45	111.19 ± 10.84a	105.71 ± 8.40	120.05 ± 7.70b	<0.001
DBP (mmHg)	65.53 ± 5.38	72.14 ± 8.57a	64.61 ± 5.89	75.25 ± 8.60b	<0.001
FBG (mmol/L)	4.55 ± 0.41	4.99 ± 0.84a	4.57 ± 0.47	4.82 ± 0.56	<0.001
TC (mmol/L)	4.01 ± 0.94	4.09 ± 1.16	4.23 ± 0.83	4.19 ± 1.06	0.478
TG* (mmol/L)	0.90 (0.75, 1.12)	0.90 (0.71, 1.20)	0.85 (0.73, 1.15)	1.26 (0.91, 1.71)b	<0.001
LDL-C (mmol/L)	2.12 ± 0.75	2.18 ± 0.76	2.36 ± 0.69	2.31 ± 0.89	0.191
HDL-C (mmol/L)	1.51 ± 0.37	1.54 ± 0.59	1.51 ± 0.34	1.35 ± 0.35	0.050
Sleep time (hours)	7.97 ± 5.15	7.41 ± 0.85	7.87 ± 1.20	7.46 ± 0.10	0.467
Physical activity (%)	130 (57.5)	113 (71.1)a	23 (74.2)	34 (59.6)	0.026

MHNW, Metabolically healthy normal weight; MUNW, Metabolically unhealthy normal weight; MHO, Metabolically healthy obesity; MUO, Metabolically unhealthy obesity; BMI, Body mass index; WC, Waist circumference; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; FBG, Fasting blood glucose; TC, Total cholesterol; TG, Triacylglycerol; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol.

* Variables are not normally distributed. ^# p for ANOVA tests or Kruskal-Wallis H test.

^a Difference between MUNW vs. MHNW.

^b Difference between MUO vs. MHO.

Fig. 1 provides the comparison of BMC and OCN differences among different metabolic phenotypes. There was a statistically significant difference between the four groups with BMC. The overweight/obesity groups showed a higher BMC than the normal weight participants, and there was a statistically significant difference between the two groups (all p < 0.05). Similar conclusions were found in boys and girls. Notably, MUNW/MUO individuals showed significantly higher BMC compared to MHNW/MHO participants among boys (p < 0.05). However, there was no statistically significant difference in OCN between different metabolic phenotypes in all subjects.

Fig. 1

Between-group comparisons of BMC, OCN according to sex. Data presented as mean ± standard deviation (OCN undergoes square root transformation). Bone mineral content (BMC), Osteocalcin (OCN), Groups: Metabolically healthy normal weight (MHNW), Metabolically unhealthy normal weight (MUNW), Metabolically healthy obesity (MHO), Metabolically unhealthy obesity (MUO). a. Difference between MUNW/MHO/MUO vs. MHNW, b. Difference between MHO/MUO vs. MUNW, and c. Difference between MUO vs. MHO.

The results of the logistic analysis are shown in Table 3. We found a significant positive correlation between BMC and MHO (except boys), and the MUO phenotype compared to the MHNW phenotype after adjusting for confounding age, sex, height, fat free mass, sleep time, and physical activity. However, OCN was not significantly associated with different metabolic phenotypes.

Table 3 Logistic regression analysis of different metabolic phenotypes and BMC, OCN in children and adolescents

	Phenotypes	N	BMC			OCN
			OR (95%CI)	Wald	p value	OR (95%CI)	Wald	p value
Total	MHNW	640	1			1
	MUNW	420	1.07 (0.57–2.00)	0.041	0.840	0.88 (0.69–1.13)	0.949	0.330
	MHO	88	8.82 (2.04–38.16)	8.480	0.004	0.91 (0.56–1.48)	0.153	0.695
	MUO	180	13.53 (4.10–44.70)	18.254	<0.001	1.28 (0.85–1.92)	1.397	0.237
Boys	MHNW	414	1			1
	MUNW	261	0.96 (0.47–1.99)	0.010	0.921	0.86 (0.63–1.18)	0.844	0.358
	MHO	57	4.86 (0.33–70.53)	1.339	0.247	0.87 (0.48–1.60)	0.195	0.658
	MUO	123	31.55 (3.66–272.16)	9.855	0.002	1.25 (0.75–2.06)	0.732	0.392
Girls	MHNW	226	1			1
	MUNW	159	1.79 (0.46–6.70)	0.702	0.402	0.91 (0.60–1.39)	0.177	0.674
	MHO	31	22.11 (2.43–201.18)	7.555	0.006	0.71 (0.30–1.68)	0.619	0.431
	MUO	57	15.70 (2.70–91.10)	9.416	0.002	0.98 (0.47–2.06)	0.002	0.966

Models are adjusted for age, gender, height and fat free mass, sleep time, physical activity.

BMC, Bone mineral content; OCN, Osteocalcin; MHNW, Metabolically healthy normal weight; MUNW, Metabolically unhealthy normal weight; MHO, Metabolically healthy obesity; MUO, Metabolically unhealthy obesity.

Table 4 illustrates the partial correlations between cardiovascular risk factors and BMC in normal weight/overweight and obesity groups. We found a positive association between BMC and BMI, WC, SBP (only in girls), DBP (only in girls), and FBG (p < 0.05), but there was a negative association between BMC and INS (only in boys), TC (only in boys), and HDL-C in normal-weight children and adolescents. BMC was positively associated with TG (only in boys) and was negatively associated with INS (only in girls) among overweight/obese individuals.

Table 4 Partial correlations between cardiovascular risk factors and BMC in normal weight/overweight and obesity

	Normal weight				Overweight and obesity
	Boys		Girls		Boys		Girls
	r	p	r	p	r	p	r	p
BMI	0.480	<0.001	0.576	<0.001	0.433	<0.001	0.254	0.028
WC	0.328	<0.001	0.451	<0.001	0.361	<0.001	0.110	0.347
SBP	0.077	0.063	0.120	0.026	–0.025	0.756	–0.041	0.727
DBP	0.056	0.175	0.178	0.001	0.046	0.575	0.051	0.661
INS	–0.155	<0.001	–0.012	0.825	–0.008	0.925	–0.231	0.046
FBG	0.098	0.018	0.146	0.007	–0.052	0.527	0.107	0.363
TC	–0.041	0.325	–0.109	0.044	–0.061	0.454	–0.001	0.995
TG	0.071	0.180	–0.013	0.809	0.172	0.034	–0.160	0.171
LDL-C	–0.065	0.119	–0.083	0.126	–0.126	0.121	–0.046	0.694
HDL-C	–0.155	<0.001	–0.174	0.001	–0.193	0.017	0.071	0.543

Adjusting age, height, fat free mass, sleep time, physical activity. Bold numbers mean that p < 0.05.

BMC, Bone mineral content; BMI, Body mass index; WC, Waist circumference; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; FBG, Fasting blood glucose; TC, Total cholesterol; TG, Triacylglycerol; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; INS, Insulin.

Discussion

Many earlier studies have analysed the relationship between bone markers and BMI, as well as cardiovascular risk factors, but few have looked at the relationship between BMC and metabolic phenotypes in children and adolescents. In this study performed in children and adolescents, we found a strongly positive correlation between MHO, and MUO phenotypes and BMC, and the MHO, MUO phenotypes had higher BMC than the MHNW or MUNW phenotypes. Meanwhile, there was no association with OCN in either boys or girls. Notably, MUO boys had higher BMC in compared to MHO.

The present study found that overweight and obese metabolically healthy or unhealthy adolescents had significantly higher BMC than normal-weight adolescents both boys and girls, which was consistent with the findings of previous studies. For example, Hetherington-Rauth et al. [12] studied 9–12 year-old girls in the USA and reported that all groups (girls were stratified by CMR group) had a significant positive relationship between fat mass and BMC. A case-control study of 112 children from Turkey showed that the obese group had higher bone mineral density (BMD) Z-scores compared with the normal weight group [21]. Another study of 154 adolescents (12–15 years, 62% females) showed that total-body-less-head (TBLH), lumbar spine (LS) and hip BMC and BMD positively correlated with weight, BMI, lean mass and fat mass [22]. In summary, obesity can protect bone health, most likely because weight-bearing exercise increases bone density, particularly in the hip and proximal femur region. Obesity can also led to hip cushioning during a fall [23]. Moreover, obesity is associated with chronic inflammation, and obese subjects have higher proinflammatory cytokines than normal-weight subjects, which promote osteoclast differentiation and bone resorption [24].

In addition, it is interesting to note that there was a significant positive correlation between BMC and the MUO phenotype compared to the MHNW phenotype after adjusting for confounding factors in boys (Table 3). And we also found that MUO individuals tended to have slightly higher BMC compared with MHO individuals among boys not in girls (Fig. 1). The findings of the current study support those of previous adult studies [13, 23]. However, Hetherington-Rauth et al. found that girls with ≥2 CMRs had a lower BMC for a given level of body fat [12]. A study with overweight adolescents aged 14–18 years found that, after controlling for confounding factors, the healthy group had 5.4% and 6.3% greater BMC than the 1 CMRs and 2 CMRs groups, respectively [25]. Studies with different results from ours suggested that age, sex, ethnicity, metabolic phenotype definition, confounding factors, and methods of measuring bone index may affect the relationship between BMC and metabolic abnormalities. In addition, we considerd that MUO groups with children and adolescents, such as middle-aged and elderly people, had higher BMC than MHO individuals, and there existed sex difference. The reason for this result, in addition to the positive correlation between BMC and BMI/WC, may also be related to the relationship between BMC and blood lipids, FBG, and insulin. We found that TG were positively correlated with BMC in overweight/obese boys. Some studies also reported a positive relationship between BMD and TG levels [26, 27]. However, the mechanisms involved in the relationship between TG and bone are unclear. The combined effect of BMI, WC, and TG may explain why the MUO phenotype has a higher odds ratio (OR) value in boys.

We observed that MHO was more positively associated with BMC than MUO, and there was no significant difference with BMC among girls between MHO and MUO groups, which may indicate that fat mass or BMI affects BMC more than cardiovascular risk factors in girls. In this study, INS showed significant negative relationships with BMC in overweight/obese girls. This is likely because girls with insulin resistance in the metabolic pathway exhibit resistance to skeletal action, causing an impairment in the IGF-I axis modulated by this hormone, leading to a low BMC in girls with MUO [28]. Hyperinsulinemia in the MUO groups reduced bone mass, thus, it is reasonable to assume that insulin weakens the positive relationship between MUO and BMC, resulting in a higher OR value in the MHO group in girls and no difference in BMC between the different metabolic status groups. Moreover, sex difference may be related to hormone secretion and body fat distribution in puberty.

After controlling for confounding factors, we did not find significant differences in BMC among normal-weight children and adolescents with different metabolic status groups, which may be related to the conflicting relationship between BMC and cardiovascular risk factors. In our study, participants with high BMC displayed higher FBG, lower HDL-C levels, and lower INS (except girls) in normal weight boys and girls. Our research is consistent or inconsistent with previous studies [29-31], and there is no conclusion about the relationship between bone and blood lipids in previous studies. This may be why there was no difference in BMC under different metabolic conditions in normal weight adolescents.

We did not find an association between serum osteocalcin and different metabolic phenotypes, and the specific mechanism is unclear. More studies are needed to explore the relationship between them. The main strength of this study is that it is the first to analyse the relationship between BMC and different metabolic phenotypes in children and adolescents. However, several limitations should be noted. First, the BMC was used in the present study because we lacked BMD data, but it is also one of the preferred measures for assessing skeletal status in adolescents because of its reproducibility and low radiation. Second, a causal relationship between BMC and metabolic status cannot be determined in cross-sectional study. Third, underweight study subjects were not excluded according to the screening criteria with WS/T-586-2018. Finally, puberty-related variables were not adjusted in the data analysis for girls because of the lack of relevant questions in our questionnaire, which may have contributed to the results.

Conclusion

This cross-sectional study showed that the MHO/MUO phenotype was positively correlated with BMC after adjusting for confounding factors. The overweight/obese children and adolescents had a higher BMC, and there existed sex differences in the effect of metabolic status on BMC among them. Since statistical difference was not detected, OCN was not supposed to be an index of bone health in this study. Nevertheless, more research is needed to demonstrate the relationship between bone health and different metabolic phenotypes in children and adolescents. In future research, more attention should be paid to the influence of abnormal cardiovascular risk factors on bone in adolescents to prevent problems before they occur.

Abbreviations

BMC, Bone mineral content; OCN, Osteocalcin; MHO, Metabolically healthy obesity; MUO, Metabolically unhealthy obesity; MUNW, Metabolically unhealthy normal weight; MHNW, Metabolically healthy normal weight; CMRs, Cardiometabolic risk factors; BMI, Body mass index; WC, Waist circumference; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; FBG, Fasting blood glucose; TC, Total cholesterol; TG, Triacylglycerol; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; INS, Insulin; BMD, Bone mineral density; PA, physical activity

Acknowledgements

This work was supported by The National Natural Science Foundation of China [grant number 82160641].

Declarations

All authors have no relevant financial or non-financial interests to disclose.

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