Mao et al. BMC Pediatrics(2024) 24:566
https://doi.org/10.1186/s12887-024-05029-x

BMC Pediatrics

RESEARCH

Open Access

Association of body composition
with ambulatory blood pressure among
Chinese youths
Bin Mao1, Jixin Zhang1, Shengnan Li1, Zehui Fan1, Ying Deng1, Hongjiao Quan1 and Yide Yang1*
Abstract
Background For youths, abnormalities in ambulatory blood pressure (ABP) patterns are known to be associatedwith increased cardiovascular disease risk and potential target organ damage. Body composition, including indicatorssuch as lean mass index (LMI), fat mass index (FMI), and visceral fat level (VFL), plays a significant role in blood pressure(BP) regulation. However, little is known about the association between these body composition indicators and ABP.
Therefore, the present study examined the association between these body composition indicators and BP among
Chinese youths.
Methods A total of 477 college students aged 17 to 28 years old (mean ± Standard deviation = 18.96 ± 1.21) froma university in Changsha, Hunan Province, China, were included in this study. Body composition indicators were
measured with a bioelectrical impedance body composition analyzer, and 24-hour ambulatory blood pressure
monitoring (ABPM) was conducted. Multivariable logistic regression was performed to assess the relationshipbetween body composition indicators and abnormal ABP.
Results The prevalence of abnormal BP, including 24-hour BP, daytime BP, nighttime BP, and clinic BP, were 4.8%,
4.2%, 8.6%, and 10.9%, respectively. After adjusting for potential covariates, LMI [abnormal 24-hour BP (OR = 1.85,
95%CI:1.31, 2.62), abnormal daytime BP (OR = 1.76, 95%CI:1.21, 2.58), abnormal nighttime BP (OR = 1.64, 95%CI:1.25,
2.14), abnormal clinic BP (OR = 1.84, 95%CI:1.38, 2.45)], FMI [abnormal 24-hour BP (OR = 1.20, 95%CI:1.02, 1.41),
abnormal daytime BP (OR = 1.30, 95%CI:1.07, 1.57), abnormal nighttime BP (OR = 1.24, 95%CI:1.10, 1.39), abnormal
clinic BP (OR = 1.42, 95%CI:1.22, 1.65)], and VFL [abnormal 24-hour BP (OR = 1.22, 95%CI:1.06, 1.39), abnormal daytime
BP (OR = 1.29, 95%CI:1.10, 1.51), abnormal nighttime BP (OR = 1.24, 95%CI:1.12, 1.39), abnormal clinic BP (OR = 1.38,
95%CI:1.21, 1.57)] are positively linked to abnormal BP. Additionally, there were significant sex differences in theassociation between body composition and abnormal BP.
Conclusions Our findings suggested maintaining an individual’s appropriate muscle mass and fat mass and focusing
on the different relations of males’ and females’ body composition is crucial for the achievement of appropriate BP
profiles.
Keywords Lean mass index, Fat mass index, Visceral fat level, Ambulatory blood pressure, Chinese youths*Correspondence:
Yide Yangyangyide2007@126.com
1
Key Laboratory of Molecular Epidemiology of Hunan Province, School of
Medicine, Hunan Normal University, Changsha 410081, China© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Mao et al. BMC Pediatrics(2024) 24:566

Introduction
In recent years, cardiovascular disease has become theleading cause of global social burden and economic burden [1]. Hypertension is the leading contributor to cardiovascular diseases such as heart attacks and strokes,
and its prevalence is progressively escalating [2]. From1999 to 2019, there has been a significant increase in theprevalence of hypertension among adults aged 30–79
globally, both in men and women. Specifically, the number of individuals in this age group with hypertension
has nearly doubled over the course of two decades [3].
Additionally, the prevalence of hypertension among Chinese adults reached 44.7% in 2017 according to a China
PEACE Million Persons Project [4]. Furthermore, it isworth mentioning that the prevalence of hypertension
was 22.1% among young Chinese by 2022 [5].
Excessive energy intake [6] and sedentary lifestyle [7],alongside individual weight gain and obesity, have been
identified as significant factors contributing to the hypertension epidemic. A meta-analysis including 57 cohort
studies has demonstrated that an escalating body massindex (BMI) is associated with elevated blood pressure(BP) and hypertension [8]. BMI is widely used as a generalindicator to evaluate overall obesity; however, it could
not accurately capture the specific distribution of muscle
and fat throughout the body as adequately as lean massindex (LMI), fat mass index (FMI) and visceral fat levels(VFL). It has been demonstrated that there is a positive
correlation between increased lean mass and hypertension [9]. Likewise, an elevated fat mass has been linked
to higher odds ratios for developing hypertension [10].
Moreover, it has been discovered that increased VFL,
reflecting the proportion of visceral fat, is connected tohigher mean BP levels [11]. Until now, the relationshipbetween 24-hour ambulatory BP (ABP) and body composition parameters has not been thoroughly studied. Previous studies investigating the relationship between bodycomposition and BP levels have predominantly relied onclinic BP measurements, which do not reflect nighttime
BP and BP’s circadian rhythmicity [12–14]. However,nighttime BP was a better predictor of cardiovasculardeath than clinic BP [15]. Twenty-four-hour ambulatory
blood pressure monitoring (ABPM) is a widely recognized gold standard for the comprehensive assessment
and diagnosis of hypertension. Unlike clinic BP measurements, ABPM offers a more detailed and accurate evaluation of BP patterns (including daytime BP, nighttime
BP, and BP circadian rhythmicity) throughout the entire
24-hour period [16, 17]. Meanwhile, previous studies on
the association between body composition and ABP wereconducted in the USA [18]and Turkey [19], whereas thestudy in China was based on old adults [20]. Research on
the youth population in China is currently insufficient.
Therefore, we aim to assess the relationship between

Page 2 of 9

body composition (LMI, FMI, and VFL) and BP indicators obtained by 24-hour ABPM among Chinese college students after controlling for potential confounders.
Meanwhile, since significant sex differences existed inboth BP and obesity indicators [21, 22], we also explored
how the relationship between body composition and
ABP differed between males and females.

Methods
Study participants

A total of 600 college students were recruited between
June 2022 and June 2023 in Changsha, Hunan province,
China. The inclusion criteria for the study participants
were as follows: (a)college students who voluntarily
expressed interest in participating in this study, (b)college students who were able to complete questionnaires, physical examinations, and clinic and ABP measurements, and (c)college students without acute illness
such as fever. Among the 600 participants involved inthis study, a total of 477 individuals (124 males and 353
females) aged 17 to 28 years old (mean ± Standard deviation = 18.96 ± 1.21) remained for this final analysis after
123 participants were excluded because of the absenceof basic demographic data (n = 59), body composition(n = 50), clinic BP data (n = 3), and complete ABPM data(n = 11). Among the 477 participants included in thisstudy, 96.6% were under the age of 22. All participants
provided informed consent before the initiation of thestudy. The current study was approved by the Medical
Research Ethical Review Committee Board of Hunan
Normal University (2019-88).
Measurements
General information

Height was measured with a stadiometer to the closest
0.1 cm with no shoes; weight was measured with a bioelectrical impedance body composition analyzer (Model
TANITA MC-780MA, Japan) to the closest 0.1 kg without wearing heavy clothing and shoes. Waist circumference (WC) was measured at 1 cm above the umbilicus
with a tape to the closest 0.1 cm. Body mass index is calculated as weight (kg)/ height squared (m2). The information, including participants of sociodemographic factors,
lifestyle factors, and parental history of hypertension,
was obtained by a self-administered questionnaire withproper instruction. The sociodemographic factors werecomposed of sex, age (years), nationality (Han nationality and other), and family per capita monthly income(CNY) (< 3000, 3000–4999, ≥ 5000, and unclear). The lifestyle factors were comprised of salt intake habits, smoking, drinking, frequency of fruit intake (< 1 time/day and≥ 1 time/day), frequency of vegetable intake (< 1 time/
day and ≥ 1 time/day) and moderate-vigorous physicalMao et al. BMC Pediatrics(2024) 24:566

activity (MVPA) (< 1 h/day and ≥ 1 h/day). The frequencyof fruit and vegetable intake was measured by the following question: How many times have you eaten fruit orvegetables in the past week?
BP measurements and body composition examinations

For clinic BP, a mercury sphygmomanometer (Yutu
XJ11D, Shanghai, China) or electronic sphygmomanometer (Type: OMRON, HBP-1320) was used with cuffs ofsuitable size applied to the right arm after 5 min of restin the sitting position. The average of three measurements was recorded as clinic BP. Abnormal clinic BP was
defined as systolic BP (SBP) ≥ 140 mmHg or diastolic BP(DBP) ≥ 90 mmHg [23].
The ABP of participants was measured using theTM-2430 blood pressure monitor (A&D, Japan), which
was applied to the non-dominant arm. The measurement
protocol included recording every 20 min during thedaytime (from 7:00 AM to 11:00 PM) and the nighttime(from 11:00 PM to 7:00 AM) recording every 30 min.
All participants successfully completed a full 24-hour
ABPM session, as specified by the European Society ofHypertension, with at least 70% of the planned readings,
including a minimum of 20 daytime readings and 7 nighttime readings [24]. Abnormal 24-hour BP was definedas SBP ≥ 130 mmHg and/or DBP ≥ 80 mmHg. Abnormal daytime BP was defined as SBP ≥ 135 mmHg and/or DBP ≥ 85 mmHg. In addition, abnormal nighttime BP
was defined as SBP ≥ 120 mmHg and/or diastolic BP ≥ 70
mmHg [25, 26].
The body composition of participants, including
total body fat, lean mass, and VFL, were obtained witha TANITA body composition analyzer (MC 780MA).
When measuring the body composition, with metal
objects removed, participants stood barefoot on the
instrument and held the handle with both hands while
waiting to complete the test. Body composition parameters, including LMI (calculated as lean body mass divided
by the square of height), FMI (calculated as fat mass
divided by the square of height), and VFL, were includedin the final analysis.
Statistical analysis

Categorical and continuous variables were describedas number (proportion) and mean (Standard deviation,SD), respectively. The sex difference was tested using
the Chi-square (χ2) test for categorical variables andthe Wilcoxon rank sum test and/or two independent
samples t-test for continuous variables. Spearman correlation analyses were conducted to examine the correlation between body composition indicators and all ABPM
components, as well as clinic BP (both SBP and DBP).
Binary logistic regression models were fitted to analyze the relationship between LMI, FMI, and VFL with

Page 3 of 9abnormal daytime/nighttime/24-hour BP and abnormal
clinic BP. In model 1, we only adjusted age. For model 2,
we further adjusted nationality, family per capita monthly
income, salt intake habits, smoking, drinking, parental
history of hypertension, frequency of fruit intake, frequency of vegetable intake, and MVPA. All models were
adjusted for sex in total population analyses. The odds
ratio (OR) and 95% confidence intervals (CIs) were usedas the estimates of parameters in the models. All analyses
were conducted using the R (version 4.2.2) and IBM SPSS(version 25.0). Two-sides test was used for all tests, andP < 0.05 was considered statistically significant.

Results
The characteristics of the study population are shown in
Table 1. A total of 477 college students (74.0% females)
were included in the final analysis. The mean WC,
BMI, LMI, and VFL were higher in males than females,
while females’ FMI was significantly higher than males(P < 0.001). The prevalence of abnormal BP, including
24-hour BP, daytime BP, nighttime BP, and clinic BP, were4.8%, 4.2%, 8.6%, and 10.9%, respectively. Males had ahigher prevalence of abnormal BP across all categories
than females (P < 0.05). Additionally, males had a higher
24-hour BP, daytime BP, nighttime BP, and clinic BP(both SBP and DBP) than females (P < 0.05). There weresignificant differences in age, nationality, drinking, and
MVPA of different sex groups.
Table 2 illustrates the correlates of body compositionindicators and all 24-hour AMBP components, and clinic
BP (both SBP and DBP). WC, BMI, LMI, and VFL werecorrelated to 24-hour BP, daytime BP, nighttime BP, andclinic BP (both SBP and DBP), with correlation coefficients ranging from 0.12 to 0.57. FMI was correlated to
24-hour DBP and clinic DBP with correlation coefficientsof 0.09 and 0.13 but was not associated with 24-hour/
daytime/nighttime/clinic SBP.
The relationship between body composition indicators and abnormal BP (24-hour, daytime, nighttime, and clinic) was presented in Table 3. After
adjustment of sex and age, LMI [abnormal 24-hour
BP (OR = 1.87, 95%CI:1.39, 2.53), abnormal daytime
BP (OR = 1.71, 95%CI:1.26, 2,31), abnormal nighttime
BP (OR = 1.58, 95%CI:1.25, 2.01), abnormal clinic BP(OR = 1.73, 95%CI:1.36, 2.20)], FMI [abnormal 24-hour
BP (OR = 1.25, 95%CI:1.08, 1.44), abnormal daytime
BP (OR = 1.29, 95%CI:1.11, 1.51), abnormal nighttime BP (OR = 1.23, 95%CI:1.10, 1.38), abnormal clinic
BP (OR = 1.39, 95%CI:1.21, 1.59)], and VFL [abnormal
24-hour BP (OR = 1.24, 95%CI:1.10, 1.39), abnormal daytime BP (OR = 1.25, 95%CI:1.11, 1.41), abnormal nighttime BP (OR = 1.23, 95%CI:1.12, 1.36), abnormal clinic
BP (OR = 1.33, 95%CI:1.19, 1.49)] are positively linked toMao et al. BMC Pediatrics(2024) 24:566

Page 4 of 9

Table 1 Characteristics of the study population
Variables
Mean (SD)/n (%)
Age (years)
Nationality
Han nationality
Other
Family per capita monthly income (yuan)< 3000
3000–4999≥5000
Unclear
Salt intake habit
High intake of salt
Moderate intake of salt
Low intake of salt
Unclear
Smoking
Yes
No
Drinking
Yes
No
Parental history of hypertension
Either parent
Neither parent or unclear
Frequency of fruit intake< 1time/day≥ 1time/day
Frequency of vegetable intake< 1time/day≥ 1time/day
MVPA< 1 h/day≥ 1 h/dayWC, cm
BMI, kg/m2
Body composition indices
LMI, kg/m2
FMI, kg/m2
VFL
Office BP mmHg
NormalAbnormal
SBP
DBP
24 h BP
NormalAbnormal
SBP
DBP
Day-time BP
NormalAbnormal
SBP

Total (n = 477)

Males (n = 124)

Females (n = 353)

P value

18.96 (1.21)

18.77 (1.07)

19.04 (1.25)

0.026

429 (89.9)
48 (10.1)

105 (84.7)
19 (15.3)

324 (91.8)
29 (8.2)

0.024

76 (15.9)
125 (26.2)
139 (29.1)
137 (28.8)

14 (11.3)
27 (21.8)
41 (33.0)
42 (33.9)

62 (17.6)
98 (27.7)
98 (27.8)
95 (26.9)

0.114

92 (19.3)
319 (66.9)
58 (12.1)
8 (1.7)23 (18.5)
88 (71.0)
10 (8.1)
3 (2.4)69 (19.5)
231 (65.5)
48 (13.6)
5 (1.4)0.344

4 (0.8)473 (99.2)

2 (1.6)122 (98.4)

2 (0.6)352 (99.4)

0.272

34 (7.1)
443 (92.9)

21 (16.9)
103 (83.1)

13 (3.7)340 (96.3)< 0.00199 (20.8)378 (79.2)

20 (16.1)
104 (83.9)

79 (22.4)274 (77.6)

0.140

391 (82.0)
86 (18.0)

106 (85.5)
18 (14.5)285 (80.7)
68 (19.3)

0.237

175 (36.7)
302 (63.3)

40 (32.3)
84 (67.7)

135 (38.2)
218 (61.8)

0.234

460 (96.0)
17 (4.0)70.30 (7.88)
21.27 (3.32)

114 (91.9)
10 (8.1)
76.31 (8.89)
22.26 (3.95)

346 (98.0)
7 (2.0)68.19 (6.25)
20.92 (2.99)

0.002< 0.001
0.001

15.82 (1.97)
5.45 (2.46)
2.96 (2.77)

18.35 (1.79)
3.92 (2.53)
4.81 (3.83)

14.93 (1.03)
5.99 (2.20)
2.31 (1.92)< 0.001< 0.001< 0.001425 (89.1)
52 (10.9)120 (14)74 (15)79 (63.7)
45 (36.3)
134 (13)77 (18)346 (98.0)
7 (2.0)116 (11)73 (13)< 0.001454 (95.2)
23 (4.8)
108 (11)66 (5)

106 (85.5)
18 (14.5)
117 (10)68 (5)

348 (98.6)
5 (1.4)104 (8)66 (5)< 0.001457 (95.8)
20 (4.2)
111 (12)107 (86.3)
17 (13.7)
122 (11)350 (99.2)
3 (0.8)107 (9)< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001

Mao et al. BMC Pediatrics(2024) 24:566

Page 5 of 9

Table 1 (continued)
Variables
Mean (SD)/n (%)
DBP
Night-time BP
NormalAbnormal
SBP
DBP

Total (n = 477)

Males (n = 124)

Females (n = 353)

P value69 (6)

72 (6)

68 (5)< 0.001436 (91.4)
41 (8.6)
100 (10)60 (6)107 (86.3)
17 (13.7)
106 (11)61 (6)

329 (93.2)
24 (6.8)
98 (9)
60 (6)

0.018< 0.001
0.069

Abbreviations WC, Waist circumference; BMI, body mass index; LMI, lean mass index; FMI, fat mass index; VFL, visceral fat level; SBP, systolic blood pressure; DBP,
diastolic blood pressure; MVPA, moderate-vigorous physical activity
Wilcoxon rank sum test and/or two independent samples t-test for r continuous variables and χ2 test for categorical variables. P value < 0.05 are set in bold

Table 2 Correlation matrix of selected variables (duplicatesomitted)
WC
BMI
LMI
FMI
VFL
24-hour SBP
24-hour DBP
Daytime SBP
Daytime DBP
Nighttime SBPNighttime DBP
Clinic SBP
Clinic DBP

WC
0.79***
0.74***
0.41***
0.80***
0.49***
0.31***
0.49***
0.33***
0.35***
0.18***
0.52***
0.29***

BMI
0.69***
0.73***
0.86***
0.36***
0.25***
0.36***
0.25***
0.25***
0.14***
0.41***
0.25***

LMI
0.08
0.63***
0.55***
0.28***
0.54***
0.30***
0.36***
0.15**
0.57***
0.26***

FMI
0.55***
0.01
0.09*
0.01
0.08
0.04
0.08
0.04
0.13*

VFL
0.43***
0.29***
0.43***
0.31***
0.27***
0.12**
0.48***
0.32****, P < 0.05; **, P < 0.01; ***, P < 0.001. WC, Waist circumference; BMI, body massindex; LMI, lean mass index; FMI, fat mass index; VFL, visceral fat level; SBP,
systolic blood pressure; DBP, diastolic blood pressure; MVPA, moderatevigorous physical activity

ABP. Even after adjustment for the potential covariates,
the associations remained significant.
Meanwhile, sex differences exist in the association
between body composition indicators and ABPM components. In males, all body composition indicators
exhibit a statistically significant association with abnormal ambulatory BP (P < 0.05). However, in females, only
LMI and VFL are significantly related to abnormal nighttime BP (P < 0.05).

Discussion
To the best of our knowledge, this is the first study of theassociation between body composition and ABP indicators conducted among relatively healthy young adults.
We found that LMI, FMI, and VFL were positively associated with abnormal BP among college students, both
based on 24-hour ABPM and clinic BP. This association
was consistent after adjusting for potential confounding factors, but there is a significant sex difference inthe association between body composition indicators
and abnormal BP phenotypes obtained from 24-hour
ABPM. Specifically, for 24 h ABPM indicators, all bodycomposition indicators are significantly associated withabnormal ABP in males (mean ± SD = 18.77 ± 1.07). However, in females (mean ± SD = 19.04 ± 1.25), only LMI and
VFL are significantly related with abnormal night-time
BP.
The prevalence of abnormal 24-hour BP in Asian adults
within a hospital-based study (mean age 57 years) was
reported as 56.4% [27], which is strikingly higher than
the 4.8% observed in this study (mean ± SD = 18.96 ± 1.21).
The discrepancy may be due to age and specific study
populations [28, 29]. Furthermore, this study found that
there are sex differences in the prevalence of abnormal
24-hour BP (14.5% in males and 1.4% in females). Such
differences may be attributed to hormones and levelsof visceral fat in the body. Estrogen can indirectly regulate BP by reducing the calcium pathway and indirectly
through genomic action [30], whereas androgens may
increase BP by activating the renin-angiotensin-aldosterone system [31]. Moreover, previous study has shownthat a high VFL is associated with the risk of hypertension [32]. In our study, males had significantly higher
VFL than females.
Although previous studies have explored the relationship between lean mass and BP, these results have been
inconsistent. A study involving 211 participants found no
significant association between lean mass percentage andelevated BP or hypertension [33]. However, another study
among 50,159 participants in America has revealed thatlean mass exhibited a significant positive correlation withhypertension in the comprehensive adjustment model[34]. Additionally, Korhonen et al. discovered that LMI
has a positive relationship with 24-hour BP components[35]. Our findings indicate that increased LMI not only
escalates the risk of abnormal clinic BP but also elevates
the risk of abnormal ABP. This relationship may result
from several mechanisms. First, the increase in lean masscontributes to enhanced vascular compliance, resulting
in the activation of the renin-angiotensin‐aldosterone
system (RAAS), consequently influencing hypertension[36, 37]. Second, high lean mass can lead to excessive
activation of the sympathetic nervous system, resulting in vasoconstriction and elevated BP [34]. Finally,1.87 (1.39, 2.53)
1.25 (1.08, 1.44)
1.24 (1.10, 1.39)

1.85 (1.31, 2.62)
1.20 (1.02, 1.41)
1.22 (1.06, 1.39)

1.80 (1.30, 2.49)
1.39 (1.15, 1.68)
1.29 (1.13, 1.48)

1.87 (1.23, 2.85)
1.35 (1.07, 1.71)
1.31 (1.09, 1.57)

2.24 (1.11, 4.53)
0.75 (0.42, 1.33)
0.82 (0.42, 1.59)

2.00 (0.79, 5.07)
0.71 (0.40, 1.26)
0.76 (0.42, 1.36)

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.
Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

< 0.001
0.001< 0.001
0.003
0.013
0.004

0.025
0.319
0.551
Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

0.001
0.030
0.005

0.143
0.245
0.349

Ref.
Ref.
Ref.

1.01 (0.29, 3,51)
0.95 (0.50, 1.83)
0.63 (0.25, 1.56)

1.17 (0.43, 3.22)
0.90 (0.51, 1.61)
0.75 (0.29, 1.94)

1.97 (1.27, 3.06)
1.37 (1.08, 1.75)
1.34 (1.11, 1.63)

1.78 (1.28, 2.46)
1.38 (1.14, 1.67)
1.28 (1.12, 1.47)

1.76 (1.21, 2.58)
1.30 (1.07, 1.57)
1.29 (1.10, 1.51)

1.71 (1.26, 2.31)
1.29 (1.11, 1.51)
1.25 (1.11, 1.41)

Abnormal day-time BP
NormalOR (95% CI)< 0.001
0.003< 0.001

P

0.984
0.886
0.313

0.757
0.732
0.548

0.002
0.010
0.003

0.001
0.001
< 0.001

0.003
0.007
0.002

0.001
0.001
< 0.001

P

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

1.81 (1.21, 2.72)
1.12 (0.94, 1.32)
1.24 (1.04, 1.47)

1.81 (1.25, 2.63)
1.14 (0.98, 1.33)
1.25 (1.07, 1.46)

1.68 (1.03, 2.75)
1.53 (1.07, 2.19)
1.31 (1.03, 1.67)

1.46 (1.08, 1.98)
1.38 (1.14, 1.67)
1.23 (1.07, 1.40)

1.64 (1.25, 2.14)
1.24 (1.10, 1.39)
1.24 (1.12, 1.39)

1.58 (1.25, 2.01)
1.23 (1.10, 1.38)
1.23 (1.12, 1.36)

Abnormal night-time BP
NormalOR (95% CI)

Ref.
Ref.
Ref.
Ref.
Ref.
Ref.

0.004
0.198
0.016

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

Ref.
Ref.
Ref.

4.32 (1.75, 10.64)
1.39 (1.10, 1.77)
1.52 (1.17, 1.97)

3.16 (1.68, 5.92)
1.39 (1.13, 1.72)
1.48 (1.20, 1.83)

1.68 (1.24, 2.29)
1.48 (1.17, 1.88)
1.39 (1.18, 1.63)

1.54 (1.21, 1.96)
1.38 (1.15, 1.64)
1.28 (1.14, 1.45)

1.84 (1.38, 2.45)
1.42 (1.22, 1.65)
1.38 (1.21, 1.57)

1.73 (1.36, 2.20)
1.39 (1.21, 1.59)
1.33 (1.19, 1.49)

Abnormal clinic BP
NormalOR (95% CI)

0.002
0.100
0.005

0.036
0.021
0.029

0.015
0.001
0.003

< 0.001
0.001< 0.001< 0.001< 0.001< 0.001

P

0.001
0.007
0.002

< 0.001
0.002< 0.001

0.001
0.001
< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001P

All models were additionally adjusted for sex in the total populations. P value < 0.05 are set in bold

Model 2: Adjusted for age, nationality, family per capita monthly income, salt intake habit, smoking, drinking, parental history of hypertension, frequency of fruit intake, frequency of vegetable intake, and MVPA. LMI, leanmass index; FMI, fat mass index; VFL, visceral fat level; BP, blood pressure; MVPA, moderate-vigorous physical activity

Model 1: Adjusted for age

Total
Model 1LMI
FMI
VFL
Model 2
LMI
FMI
VFL
Males
Model 1LMI
FMI
VFL
Model 2LMI
FMI
VFL
FemalesModel 1LMI
FMI
VFL
Model 2
LMI
FMI
VFL

Abnormal 24 h BP
NormalOR (95% CI)

Table 3 Multinomial logistic regression models evaluating the associations of body composition indicators with risk of abnormal BP based on 24 h ABPM and clinic BP

Mao et al. BMC Pediatrics(2024) 24:566
Page 6 of 9

Mao et al. BMC Pediatrics(2024) 24:566

LMI is mainly composed of skeletal muscle; the expansion of skeletal muscle tissue accompanying lean bodymass growth consequently contributes to an augmentation in blood volume, thereby resulting in elevated bloodpressure [38]. However, excessive skeletal muscle mass
throughout the body has received little concern, so thatits adverse effects on BP have been overlooked. Appropriate systemic skeletal muscle levels may be crucial for
maintaining normal BP, especially ABP.
A previous study of 4,864 Korean adults proved that
increased fat mass is related to an increased risk ofhypertension [39]. In addition, fat mass plays a crucialrole in influencing an individual’s BP in obese and nonobese children [40]. Our findings are consistent withprevious studies. Consider that fat mass and BP are
influenced by some factors, such as physical activity anddietary habits/behavior. Therefore, we made a relatively
comprehensive adjustment for potential confounding
factors. In our study, we found that FMI was positively
related to the risk of abnormal ABP and clinic BP. Thesefindings may be partly explained by abnormalities in the
RAAS, elevated insulin resistance, increased sympathetic tone, and release of inflammatory factors [41–43].
Besides, the relationship between VFL and abnormal
ABP and clinic BP is statistically significant in this study.
A review has demonstrated that excessive accumulationof visceral fat has been implicated in the pathogenesis ofchronic inflammation and metabolic disorders, including elevated blood pressure [44]. In fact, the underlying mechanism of the positive correlation between VFL
and BP is consistent with that of the positive correlation
between FMI and BP, both stemming from abnormalities
in the RAAS and the release of inflammatory factors [45].
Therefore, it might be important that FMI and VFL, asrisk factors for abnormal BP, are controlled at reasonablelevels to improve an individual’s BP profiles.
Notably, the sex discrepancy in the relationshipbetween body composition indicators and abnormal
BP based on 24-hour ABPM was found in this study. In
males, all body composition indicators exhibit a statistically significant association with abnormal ABP. However, in females, only LMI and VFL are significantly
related to abnormal ABP. These findings suggest thatdifferent body composition indices may influence ABPpatterns in a sex-specific manner. Because the default
settings for daytime and nighttime ABPM may not accurately align with the participants’ actual periods of activity and sleep. It is, therefore, critical to consider thisdifference, as diurnal BP rhythms are regulated by theautonomic nervous system, and various factors such as
diet and physical activity can significantly influence ABPlevels. Due to the variances in factors such as diet, physical activity, and other determinants between males andfemales, it is plausible that their circadian BP rhythms

Page 7 of 9

differ as well. Consequently, these differences in circadian
BP patterns may influence the association between bodycomposition and ABP. In addition, hormones like estrogen and testosterone have different effects on BP regulation [46]. For example, estrogen may have a protective
effect on cardiovascular health in premenopausal females[46]. Furthermore, socioeconomic status can influence
access to healthcare and preventive measures, which
may differ between males and females [47]. Due to thesevariances, it is plausible that circadian BP rhythms differ
between males and females, influencing the association
between body composition indicators and ABP. However,the underlying mechanism for this difference is unclear.
Future studies should aim to identify the underlying
mechanisms contributing to this sex difference.
The strength of this study is to investigate the relationship between body composition and ABP in college
students who are more representative of young Chinesegroups. That’s the novelty of our study. The outcomes ofprevious studies were mostly focused on clinic BP [48,49]. Even though there are studies that concentrate on
ABP, the population is based on older adults [35]. However, we also acknowledge some limitations in this study.
Firstly, the type of study was cross-sectional, and a causal
link between body composition and BP levels could not
be determined. More cohort studies or intervention studies are needed to assess these relationships. Secondly,
only participants ranging in age from 17 to 28 years were
included in our study. Moreover, the study is largely relevant for Chinese youths ≤ 21 years old despite a small
percentage of older participants. Therefore, we should
be cautious when generating the findings for other age
groups.

Conclusion
In conclusion, LMI, FMI, and VFL might be risk factors
for abnormal BP based on 24-hour ABPM and clinic BP
after adjusting for the potential covariates. In addition,
our results indicate that there are significant sex differences in the association between body compositionindicators (LMI, FMI, and VFL) and abnormal ABPM
components. LMI, FMI, and VFL are all risk factors forabnormal BP based on ABPM in males, but in females,
LMI and VFL are significantly associated with abnormalnight-time BP. Our findings suggested maintaining an
individual’s appropriate muscle mass and fat mass andfocusing on the different relations of males’ and females’
body composition is crucial for the achievement ofappropriate BP profiles.
Acknowledgements
We would like to express our sincere gratitude to all the participants who took
part in this study.

Mao et al. BMC Pediatrics(2024) 24:566

Author contributions
Bin Mao: Conceptualization, Data Curation, Formal Analysis, Methodology,
Writing - Original Draft and Final Paper. Shengnan Li: Project administration,
Resources, Supervision. Jixin Zhang: Data curation, Investigation, Validation.
Zehui Fan: Conceptualization, Formal analysis, Methodology. Ying Deng: Data
curation, Investigation. Hongjiao, Quan: Formal analysis, Methodology. Yide
Yang: Project administration, Resources, Supervision, Validation, Writing –original draft, Writing – review & editing.
Funding
This study was supported by the National Natural Science Foundation ofChina (No. 81903336), a project supported by the Scientific Research Fundof Hunan Provincial Education Department (No. 22B0038, 22C0025, and
22C0027), Scientific Research Project of Hunan Health Committee (No.
202112031516), Postgraduate Scientific Research Innovation Project of Hunan
Province (QL20230133), and Hunan province college students researchlearning and innovative experiment project (S202410542075).
Data availability
The datasets used and/or analyzed during the current study are available fromthe corresponding author (yangyide2007@126.com) on reasonable request.

Declarations
Ethics approval and consent to participate
The current study was approved by the Medical Research Ethical Review
Committee Board of Hunan Normal University (2019-88).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no conflict of interest.
Received: 27 January 2024 / Accepted: 23 August 2024

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