Association Between Body Composition and Retinal Vascular Caliber in Children and Adolescents

Abstract

Purpose.: To elucidate the relationship between body composition and retinal vascular caliber (RVC) in children and adolescents.

Methods.: Participants aged 7 to 19 years were recruited from the Guangzhou Twin Eye Study cohort. Retinal vascular caliber was measured cross-sectionally from retinal images using a computer-aided program. The data were expressed as the central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE). Triceps skinfold thickness (TSFT) was measured using a skinfold caliper. Body composition was obtained through bioelectrical impedance analysis, providing fat mass (FM), fat mass index (FMI), fat-free mass (FFM), fat-free mass index (FFMI), body water mass (BWM), body water percentage (BWP), basal metabolic rate (BMR), and trunk fat percentage (TFP).

Results.: A total of 731 participants were included. Among the younger children (7–11 years), there was no significant association between body composition and RVC (all P > 0.05). However, for the older children (12–19 years), increasing FM, FMI, TFP, TSFT, and BMI were associated with CRAE negatively (β = −0.20, −0.68, −0.18, −0.17, and −0.48, respectively, all P < 0.05) and with CRVE positively (β = 0.47, 1.26, 0.34, 0.37, and 0.78, respectively, all P < 0.05), after adjusting for age, sex, axial length, mean arterial blood pressure, and the fellow vascular diameter. In contrast, increase in BWP was associated with larger CRAE (β = 0.33, P = 0.001) and smaller CRVE (β = −0.64, P < 0.001). Similar associations also were observed in the analyses when the body composition parameters were divided into quartiles (all P < 0.05).

Conclusions.: In adolescents, greater body fat deposition is related to narrower retinal arterioles and wider retinal venules, and higher body water proportion is associated with retinal arterioles widening and retinal venules narrowing. Even during childhood, body composition might have an association with systemic microvasculature.

Introduction

In the latest 2 decades, childhood obesity has been recognized as a global public health problem. Childhood obesity and overweight increase the risk of cardiovascular disease, and thus mortality.¹ The definition of obesity among children is a body mass index (BMI; the weight in kilograms divided by the square of the height in meters) greater than the age- and sex-specific 95th percentile. However, BMI provides no information on body composition and is not sensitive to changes in adipose tissue during childhood.² Specific biological body components (e.g., fat mass [FM], fat-free mass [FFM], body water mass [BWM], and trunk fat percentage [TFP]) have been increasingly adopted in recent clinical and epidemiological research on obesity and its related diseases.³ 

The retinal vasculature has been recognized as a surrogate for systemic microcirculation. Examination of the ocular fundus provides a unique, noninvasive approach to assess the status of microcirculation.⁴ Studies have shown that narrower retinal arteriolar caliber is related to hypertension,⁵ stroke,⁶ and coronary artery disease,⁷ whereas wider retinal venular caliber is associated with systemic inflammation⁸ and obesity.⁹ Among children, population-based studies have shown that greater BMI and body weight are associated with narrower retinal arteriolar diameter and larger retinal venular diameter,^10,11 indicating a possible effect of obesity on early microvascular structural changes in childhood. However, the relationship between body composition and retinal vascular caliber (RVC) remains obscure. Given that measurements for body composition offer a more detailed description of the biological body components, we hypothesize that body composition, especially the body fat distribution, is associated with retinal microvasculature. We tested our hypothesis in a study of Chinese children and adolescents aged 7 to 19 years old. 

Methods

Study Population

Subjects in the present study were recruited from the Guangzhou Twin Registry, a population-based twin cohort study in Guangzhou, China.^12,13 In brief, approximately 9700 twin pairs born in Guangzhou between 1987 and 2000 were identified with an official household registry followed by door-to-door confirmation. Data collection began in 2006 and children aged 7 to 19 years were offered an examination in July 2009, providing cross-sectional data on RVC and body composition. Provided that the first-born and second-born twin data were correlated with each other (Supplementary Table S2), the 1281 first-born twins were arbitrarily selected for analysis to ensure sample independence. Eight twin pairs with systemic (two with cerebral palsy) or ocular abnormalities (three with cataract and three with retinopathy of prematurity) were excluded. Individuals with missing data (362 without body composition parameters and 226 without RVC measures) were excluded as well, leaving 731 subjects in our present study. The study adhered to the tenets of the Declaration of Helsinki. All procedures were approved by the Ethics Committee of the Zhongshan Ophthalmic Center. Written informed consent was obtained from the parents or legal guardians of all subjects, with assent secured from the children themselves. 

Retinal Vascular Caliber Measurements

Children were examined at Zhongshan Ophthalmic Center in July 2009. After mydriasis with cyclopentolate 1%, digital retinal photographs centered on the optic disc were taken with a digital retinal camera (Nonmyd 7 digital fundus camera; Kowa, Tokyo, Japan) in a standardized fashion. The methods for evaluation of retinal vascular caliber from digitized retinal photographs have been published elsewhere.¹⁴ Briefly, based on the computer-assisted software (IVAN; University of Wisconsin, Madison, WI, USA) and a standardized protocol provided by the Retinal Vascular Imaging Centre (University of Melbourne, Melbourne, Australia), trained graders, who were masked to participant characteristics and parameters of body composition, measured the diameter of all the retinal arterioles and venules coursing through a zone 0.5 to 1.0 disc diameter from the optic disc margin. These measurements were expressed as central retinal arteriolar equivalent (CRAE) and venular equivalent (CRVE) to the nearest 0.1 μm, representing the average retinal arteriolar and venular caliber, respectively.¹⁴ Fifty retinal images randomly selected were reevaluated 4 weeks later by the same grader, with intraclass correlation coefficients greater than 0.90 for both retinal arteriolar caliber and venular caliber. 

Anthropometric Measurements and Body Composition Analysis

The term “obesity” is mostly defined according to established classification metrics based on BMI, whereas “adiposity” is defined as the degree of body fat accumulation, and is generally used to denote excess body fat (e.g., higher body fat, FMI).¹⁵ In our present study, we obtained several adiposity-related measures and analyzed their association with RVC. First, a Lange skinfold caliper was used to measure triceps skinfold thickness (TSFT) with a pressure of 10 g/mm² of contact surface area. The measurement was conducted on the back of the arm and midway between the point of the acromion and olecranon process when the arm was hanging relaxed. The readings were recorded to the nearest 0.5 mm. The mean of the three readings was used for further analysis. Second, body composition parameters were collected via foot-to-foot bioelectrical impedance analysis (BIA) using a Tanita TBF-418B analyzer (Tanita Corp., Tokyo, Japan) according to both the instructions of the manufacturer and the 2004 guidelines of the European Society for Parenteral and Enteral Nutrition for the use of BIA measurements.¹⁶ Measurements were taken at least 2 hours after ingestion of breakfast, with previous voiding of the urinary bladder. Data on age, sex, and body type (athletic or normal) of the subject were recorded, and the analyzer automatically measured body height, weight, body impedance (in ohms), and the following parameters related to body composition: basal metabolic rate (BMR) in kcal; FM, FFM, and BWM to the nearest 0.1 kg; and TFP and body water percentage (BWP) to 0.1%. Fat mass index (FMI) and fat-free mass index (FFMI) were calculated, respectively, as FM (kg) divided by height squared (m²) and FFM (kg) divided by height squared (m²). Mean arterial blood pressure (MAP) was defined as diastolic blood pressure plus one-third of pulse pressure. 

Statistical Methods

In our initial analysis, we observed significant interaction of age category (age < 12 years versus age ≥ 12 years) and sex on the retinal vessel calibers. Moreover, given the physical growth (i.e., puberty) across the study population, we stratified them into two growth subgroups: the younger age group (7–11 years) and the older age group (12–19 years). In multivariable regression models, CRAE or CRVE were treated as dependent variables to assess their relationship with various potential predictors: FM, FMI, FFM, FFMI, BWM, BWP, BMR, TSFT, TFP, and BMI with adjustment for age, sex, MAP, axial length, and the fellow vascular diameter (i.e., CRVE for CRAE outcomes and vice versa) that had been identified as a covariate associated with RVC in children in previous studies.^17,18 Additionally, we divided body composition measures (FM, FMI, FFMI, BWP, TSFT, TFP, and BMI) into quartiles to assess the association between body mass and retinal vessel diameter. For testing of statistical significance, we set the P value threshold at P = 0.05. To adjust for multiple comparisons, we further consider P = 0.005, the threshold based on Bonferonni correction for simultaneously testing the significance of the above 10 predictors in the multivariate analyses. Statistical analyses were undertaken using Stata software (version 12.0; StataCorp LP, College Station, TX, USA). 

Results

Descriptive Statistics

This study included 287 children (133 boys and 154 girls) aged 7 to 11 years old and 444 adolescents (206 boys and 238 girls) aged 12 to 19 years old (Table 1). No significant difference was found in mean age and MAP between boys and girls in either subgroup. The differences in axial length, TSFT, and all body composition parameters between boys and girls were significant (P < 0.05, Table 1). Specifically, boys tended to have higher FFM, FFMI, BWP, and BMR, whereas girls had greater FM, FMI, TFP, and TSFT. Both CRAE and CRVE were approximately normally distributed (data not shown). Compared with girls, boys had both smaller CRAE (149.6 ± 13.7 μm versus 153.4 ± 12.8 μm, P = 0.014 in children; 149.6 ± 13.7 μm versus 150.9 ± 12.1 μm, P < 0.001 in adolescents) and smaller CRVE (216.1 ± 17.6 μm versus 221.7 ± 18.3 μm, P = 0.009 in children; 213.1 ± 20.9 μm versus 217.5 ± 18.7 μm, P = 0.0190 in adolescents). 

Table 1.

 

Characteristics of Study Population

Characteristic	Younger Age Group, 5–11 y, n= 287			Older Age Group, 12–19 y, n= 444
	Boys, n= 133	Girls, n= 154	P	Boys, n= 206	Girls, n= 238	P
Age, y	9.62 ± 1.36	9.42 ± 1.25	0.191	14.3 ± 2.04	14.5 ± 2.00	0.288
Fat mass, kg	6.56 ± 5.31	7.90 ± 4.60	0.025	8.25 ± 5.70	12.6 ± 4.69	<0.001
Fat mass index, kg/m2	2.82 ± 2.26	3.53 ± 1.85	0.004	2.90 ± 1.97	5.05 ± 1.83	<0.001
Fat-free mass, kg	34.4 ± 7.58	30.4 ± 5.20	<0.001	48.7 ± 5.57	36.2 ± 3.64	<0.001
Fat-free mass index, kg/m2	15.0 ± 1.12	13.8 ± 1.12	<0.001	17.2 ± 1.57	14.6 ± 1.04	<0.001
Body water mass, kg	25.1 ± 5.52	22.2 ± 3.81	<0.001	34.9 ± 3.99	24.0 ± 2.75	<0.001
Body water percentage, %	62.5 ± 6.28	59.1 ± 4.88	<0.001	62.0 ± 5.27	53.8 ± 4.36	<0.001
Basal metabolic rate, kJ	1367.3 ± 177.1	1269.6 ± 149.6	<0.001	1563.1 ± 174.7	1267.8 ± 137.9	<0.001
Trunk fat percentage, %	12.5 ± 8.99	15.6 ± 7.96	0.002	11.9 ± 7.24	22.6 ± 6.60	<0.001
TSFT, mm	13.8 ± 7.00	15.8 ± 5.96	0.010	13.9 ± 6.93	19.4 ± 5.90	<0.001
MAP, mm Hg	69.7 ± 10.5	69.6 ± 9.31	0.956	78.7 ± 9.40	77.0 ± 10.0	0.075
Axial length, mm	24.5 ± 1.15	23.8 ± 1.06	<0.001	24.9 ± 1.24	24.4 ± 1.13	<0.001
BMI, kg/m2	17.8 ± 3.06	17.4 ± 2.87	0.207	20.1 ± 3.23	19.7 ± 2.70	0.148
CRAE, μm	149.6 ± 13.7	153.4 ± 12.8	0.014	146.1 ± 14.0	150.9 ± 12.1	<0.001
CRVE, μm	216.1 ± 17.6	221.7 ± 18.3	0.009	213.1 ± 20.9	217.5 ± 18.7	0.019

 Data are mean ± SD, and compared using t-test unless otherwise stated. TSFT, triceps skinfold thickness; MAP, mean arterial blood pressure; BMI, body mass index; CRAE, central retinal arteriolar equivalent; CRVE, central retinal venular equivalent.

Multivariable Linear Regression

In the younger age (7–11 years) group, we found no association between body composition parameters and either CRAE or CRVE (P > 0.05; Table 2; Supplementary Table S1). However, in the older age (12–19 years) group (Table 3; Supplementary Table S1), there was significant retinal arteriolar narrowing with the increases of FM, FMI, FFMI, TFP, TSFT, and BMI (β_FM = −0.20, P = 0.027; β_FMI = −0.68, P = 0.007; β_FFMI = −0.91, P = 0.013; β_TFP = −0.18, P = 0.010; β_TFST = −0.17, P = 0.021; β_BMI = −0.48, P = 0.003) after adjusting for age, sex, axial length, MAP, and CRVE. Higher FM, FMI, TFP, TSFT, and BMI (β_FM = 0.47, P = 0.001; β_FMI = 1.26, P = 0.001; β_TFP = 0.34, P = 0.001; β_TFST = 0.37, P = 0.001; β_BMI = 0.78, P = 0.002) were related to the widening of retinal venules significantly. On the contrary, greater BWP was associated with larger CRAE (β = 0.33, P = 0.001) and smaller CRVE (β = −0.64, P < 0.001) significantly. When Bonferroni correction is applied (P value threshold = 0.005), only BMI and BWP remain significantly associated with retinal arteriolar narrowing, while FMI becomes marginally significant (P = 0.007). The list of significant predictors for retinal venules widening, however, remains unchanged. 

Table 2

 

Relationship Between Body Composition and RVC in 7- to 11-Year-Old Children

Body Composition Parameters	Retinal Arteriolar Caliber, μm				Retinal Venular Caliber, μm
	Age and Sex Adjusted		Multiple Linear Regression*		Age and Sex Adjusted		Multiple Linear Regression*
	β Coefficient (95% CI)	P	β Coefficient (95% CI)	P	β Coefficient (95% CI)	P	β Coefficient (95% CI)	P
Fat mass, kg	−0.26 (−0.57 to 0.05)	0.104	−0.07 (−0.34 to 0.20)	0.618	−0.10 (−0.53 to 0.33)	0.644	0.14 (−0.23 to 0.52)	0.453
Fat mass index, kg/m2	−0.62 (−1.35 to 0.13)	0.103	−0.19 (−0.83 to 0.45)	0.562	−0.27 (−1.31 to 0.76)	0.598	0.27 (−0.62 to 1.16)	0.549
Fat-free mass, kg	−0.28 (−0.62 to 0.06)	0.102	−0.002 (−0.30 to 0.29)	0.990	−0.24 (−0.71 to 0.23)	0.319	0.09 (−0.33 to 0.51)	0.671
Fat-free mass index, kg/m2	−0.28 (−1.79 to 1.22)	0.708	0.13 (−1.17 to 1.42)	0.849	0.21 (−1.88 to 2.29)	0.843	0.75 (−1.05 to 2.54)	0.414
Body water mass, kg	−0.40 (−0.87 to 0.06)	0.088	−0.01 (−0.42 to 0.40)	0.969	−0.35 (−0.99 to 0.29)	0.283	0.12 (−0.45 to 0.69)	0.688
Body water percentage, %	0.24 (−0.03 to 0.51)	0.087	0.08 (−0.15 to 0.32)	0.493	0.13 (−0.25 to 0.50)	0.512	−0.08 (−0.40 to 0.25)	0.640
Basal metabolic rate, kJ	−0.01 (−0.02 to 0.002)	0.095	0.002 (−0.008 to 0.01)	0.723	−0.01 (−0.03 to 0.003)	0.111	−0.001 (−0.02 to 0.01)	0.858
Trunk fat percentage, %	−0.18 (−0.36 to 0.003)	0.054	−0.06 (−0.22 to 0.09)	0.426	−0.09 (−0.34 to 0.15)	0.461	0.06 (−0.15 to 0.28)	0.551
TSFT, mm	−0.10 (−0.34 to 0.13)	0.391	0.05 (−0.15 to 0.26)	0.604	−0.10 (−0.42 to 0.23)	0.555	0.03 (−0.25 to 0.31)	0.818
Body mass index, kg/m2	−0.38 (−0.91 to 0.15)	0.157	−0.09 (−0.55 to 0.37)	0.697	−0.17 (−0.90 to 0.57)	0.658	0.20 (−0.44 to 0.84)	0.542

 CI, confidence interval.

*  Multiple linear regression is adjusted for age, sex, axial length, the fellow vessel diameter, and MAP.

Table 3

 

Relationship of Body Composition Parameters to RVC in 12- to 19-Year-Old Adolescents

Body Composition Parameters	Retinal Arteriolar Caliber, μm				Retinal Venular Caliber, μm
	Age and Sex Adjusted		Multiple Linear Regression*		Age and Sex Adjusted		Multiple Linear Regression*
	β Coefficient (95% CI)	P	β Coefficient (95% CI)	P	β Coefficient (95% CI)	P	β Coefficient (95% CI)	P
Fat mass, kg	−0.19 (−0.42 to 0.04)	0.114	−0.20 (−0.24 to −0.05)	0.027	0.33 (−0.02 to 0.68)	0.066	0.47 (0.19 to 0.74)	0.001
Fat mass index, kg/m2	−0.57 (−1.19 to 0.06)	0.076	−0.68 (−1.17 to −0.19)	0.007	0.90 (−0.06 to 1.86)	0.067	1.26 (0.52 to 2.01)	0.001
Fat-free mass, kg	−0.08 (−0.34 to 0.18)	0.561	−0.02 (−0.23 to 0.20)	0.883	0.24 (−0.16 to 0.64)	0.232	0.30 (−0.20 to 0.62)	0.069
Fat-free mass index, kg/m2	−0.73 (−1.64 to 0.18)	0.116	−0.91 (−1.63 to −0.19)	0.013	0.93 (−0.47 to 2.33)	0.191	1.09 (−0.02 to 2.20)	0.055
Body water mass, kg	−0.07 (−0.42 to 0.28)	0.707	0.04 (−0.24 to 0.33)	0.767	0.22 (−0.32 to 0.76)	0.428	0.24 (−0.20 to 0.67)	0.279
Body water percentage, %	0.26 (−0.004 to 0.52)	0.054	0.33 (0.13 to 0.54)	0.001	−0.45 (−0.85 to −0.05)	0.028	−0.64 (−0.95 to −0.33)	<0.001
Basal metabolic rate, kJ	−0.003 (−0.01 to 0.005)	0.465	−0.002 (−0.01 to 0.004)	0.640	0.008 (−0.004 to 0.02)	0.194	0.01 (−0.01 to 0.02)	0.065
Trunk fat percentage, %	−0.16 (−0.33 to 0.01)	0.066	−0.18 (−0.31 to −0.04)	0.010	0.22 (−0.05 to 0.48)	0.109	0.34 (0.14 to 0.55)	0.001
TSFT, mm	−0.12 (−0.31 to 0.06)	0.192	−0.17 (−0.32 to −0.03)	0.021	0.30 (0.01 to 0.58)	0.040	0.37 (0.15 to 0.59)	0.001
BMI, kg/m2	−0.37 (−0.77 to 0.04)	0.076	−0.48 (−0.79 to −0.16)	0.003	0.61 (−0.004 to 1.22)	0.052	0.78 (0.30 to 1.27)	0.002

*  Multiple linear regression is adjusted for age, sex, axial length, the fellow vessel diameter, and MAP.

The relationship between RVC and quartiles of body composition parameters in 12- to 19-year-old adolescents is shown in Table 4. With the increasing quartiles of FM, FMI, TFP, TSFT, and BMI, arteriolar diameter tended to decrease (P = 0.045, 0.005, 0.009, 0.007, and 0.026, respectively), while venular diameter tended to increase (P = 0.005, 0.005, 0.001, 0.002, and 0.020, respectively) after adjusting for age, sex, axial length, MAP, and the fellow vessel diameter. In contrast, increasing BWP was associated with wider retinal venules (P = 0.009) and narrower retinal venular diameter (P = 0.001). 

Table 4

 

Retinal Vessel Calibers by Quartiles of Body Composition Parameters in 12- to 19-Year-Old Adolescents

	n= 444	CRAE, μm		CRVE, μm
		Age and Sex Adjusted Mean (95% CI)	Multiple Linear Regression* Mean (95% CI)	Age and Sex Adjusted Mean (95% CI)	Multiple Linear Regression* Mean (95% CI)
Fat mass, kg
First quartile, <6.6	111	147.3 (140.8–153.8)	147.5 (129.4–165.6)	211.8 (204.4–219.2)	212.5 (184.7–240.3)
Second quartile, 6.6–9.9	111	149.5 (141.5–157.5)	148.9 (131.3–166.5)	215.2 (207.3–223.1)	213.4 (188.3–238.5)
Third quartile, 9.9–13.6	111	149.8 (143.3–156.3)	150.5 (133.7–167.3)	217.1 (210.2–224.0)	218.7 (195.9–241.5)
Fourth quartile, ≥13.6	111	148.1 (141.7–154.5)	147.8 (129.5–166.1)	217.8 (211.1–224.5)	217.5 (190.6–244.4)
P for trend		0.169	0.045	0.110	0.005
Fat mass index, kg/m2
First quartile, <2.36	111	147.0 (140.9–153.1)	147.2 (128.1–166.3)	211.4 (204.3–218.5)	211.7 (182.6–240.8)
Second quartile, 2.36–3.87	112	149.8 (141.8–157.8)	149.5 (132.8–166.2)	215.3 (208.0–222.6)	215.1 (190.8–239.4)
Third quartile, 3.87–5.28	110	149.9 (143.3–156.5)	149.5 (130.7–168.3)	217.2 (210.7–223.7)	217.1 (192.7–241.5)
Fourth quartile, ≥5.28	111	148.2 (141.4–155.0)	148.4 (131.8–165.0)	217.9 (211.0–224.8)	218.2 (193.2–243.2)
P for trend		0.088	0.005	0.109	0.005
Fat-free mass index, kg/m2
First quartile, <14.5	110	151.1 (145.8–156.4)	151.0 (136.6–165.4)	216.0 (208.7–223.3)	215.6 (193.9–237.3)
Second quartile, 14.5–15.5	112	150.0 (144.3–155.7)	149.9 (133.3–166.5)	216.6 (207.9–225.3)	217.2 (192.6–241.8)
Third quartile, 15.5–16.9	111	147.9 (141.5–154.3)	148.2 (130.8–165.6)	215.2 (205.2–225.2)	215.9 (189.5–242.3)
Fourth quartile, ≥16.9	111	145.8 (140.2–151.4)	145.4 (125.2–165.6)	214.1 (206.2–222.0)	213.4 (183.1–243.7)
P for trend		0.961	0.374	0.064	0.048
Body water percentage, %
First quartile, <53.1	111	148.2 (141.8–154.6)	148.3 (132.4–164.2)	217.8 (209.2–226.4)	218.9 (193.4–244.4)
Second quartile, 53.1–57.4	110	150.5 (144.2–156.8)	150.8 (133.8–167.8)	217.9 (210.3–225.5)	217.4 (194.5–240.3)
Third quartile, 57.4–62.6	115	148.5 (139.5–157.5)	147.7 (126.0–169.4)	214.2 (205.3–223.1)	212.7 (182.9–242.5)
Fourth quartile, ≥62.6	108	147.5 (143.0–151.9)	147.9 (132.4–163.4)	211.9 (205.1–218.7)	213.2 (188.1–238.3)
P for trend		0.343	0.009	0.007	0.001
Trunk fat percentage, %
First quartile, <10.5	111	146.9 (141.2–152.6)	147.0 (128.3–165.7)	211.2 (204.2–218.2)	211.7 (183.3–240.1)
Second quartile, 10.5–17.9	111	148.8 (140.6–157.0)	148.5 (131.4–165.6)	214.6 (207.3–221.9)	213.8 (171.0–256.6)
Third quartile, 17.9–23.5	110	150.3 (143.9–156.7)	150.4 (131.7–169.1)	217.5 (210.6–224.4)	217.9 (193.8–242.0)
Fourth quartile, ≥23.5	112	148.8 (142.4–155.2)	148.7 (132.4–165.0)	218.5 (211.6–225.4)	218.8 (193.8–243.8)
P for trend		0.257	0.009	0.027	0.001
TSFT, mm
First quartile, <11.5	108	147.7 (141.0–154.4)	147.7 (130.5–164.9)	211.4 (204.4–218.4)	211.5 (187.3–235.7)
Second quartile, 11.5–17.0	109	149.2 (141.1–157.3)	148.8 (130.1–167.5)	214.6 (206.6–222.6)	213.7 (185.4–242.0)
Third quartile, 17.0–21.5	114	149.3 (142.5–156.1)	149.9 (133.2–166.6)	216.8 (209.9–223.7)	218.6 (195.1–242.0)
Fourth quartile, ≥21.5	113	148.6 (141.9–155.3)	148.2 (129.5–166.9)	218.8 (212.0–225.6)	218.0 (191.5–244.5)
P for trend		0.239	0.007	0.029	0.002
BMI, kg/m2
First quartile, <18.0	92	149.9 (143.0–156.8)	149.4 (135.1–163.7)	213.3 (205.4–221.2)	213.1 (190.8–235.4)
Second quartile, 18.0–19.0	64	149.0 (141.9–156.1)	149.1 (133.4–164.8)	214.0 (205.3–222.7)	213.0 (188.0–238.0)
Third quartile, 19.0–21.0	143	148.7 (141.4–156.0)	149.6 (129.3–169.9)	216.0 (207.4–224.6)	217.5 (189.6–245.3)
Fourth quartile, ≥21.0	145	147.8 (141.3–154.3)	147.1 (129.2–165.0)	216.9 (209.1–224.7)	216.3 (189.9–242.6)
P for trend		0.323	0.026	0.070	0.020

*  Multiple linear regression is adjusted for age, sex, axial length, the fellow vessel diameter, and MAP.

Discussion

The current study on a population of healthy children and adolescents demonstrated the associations between body composition and RVC. For children older than 11 years, greater body fat deposition (i.e., higher FM, FMI, TFP, and TSFT) is generally related to narrower retinal arterioles and wider retinal venules. In contrast, a larger proportion of body water is associated with wider retinal arterioles and narrower retinal venules. Such associations are not significant in children aged from 7 to 11 years old. These data offer new evidence to support the hypothesis that obesity may be related to the microvascular system even during the childhood and teenage years. 

Several population-based studies have reported an association between obesity and changes of RVC in both adults and children, but the results are inconsistent.^10,19 Some studies suggest that greater BMI is associated with wider retinal venular caliber, whereas others show that greater BMI is associated with not only smaller CRAE but also larger CRVE (present study, Supplementary Table S1).²⁰ Specific to children, higher BMI is related to smaller CRAE and greater CRVE in both 6- and 12-year-old children in Sydney.^11,21 A recent meta-analysis summarized 12 studies from diverse ethnicities and found that a higher BMI is associated with narrower retinal arteriolar and wider venular calibers.²² Similarly, we found that greater body fat deposition, represented by higher FM, FMI, TFP, and TSFT, was associated with narrower retinal arterioles and wider venules. However, these trends were present only in the older (12–19 years) children, but not in the younger age (7–11 years) group, implying that obesity may have association with microvasculature in children reaching puberty. These findings provide supportive evidence linking a high level of adiposity to early microvascular changes. 

In current clinical and epidemiological practice, BMI is widely used to categorize underweight, normal weight, overweight, and obesity³; however, this traditional measurement has its weaknesses. First, as each organ and tissue has its own specific composition, BMI is unable to reflect fat distribution in a human body precisely. Second, BMI does not take specific composition of whole body weight into account; thus, it is unable to distinguish adipose tissue deposition from muscular hypertrophy. This can be best exemplified by a subgroup of normal-weight subjects, who show low subcutaneous but increased visceral FM, and have increased cardio-metabolic risk in spite of their normal BMI.²³ By contrast, approximately 30% of obese subjects appeared to have a favorable metabolic profile (i.e., absence of metabolic complications, dyslipidemia, hypertension, and systemic inflammation), and therefore should be considered as “metabolically healthy obese” subjects.²⁴ As for children, BMI also has weakness in delineating the body changes with physical growth. Some research reports that there is a 2-fold range of variation in fatness for a given BMI value in individual children.²⁵ Therefore, detailed measurements combining individual body components with their related functional aspects should be introduced into epidemiological studies regarding obesity and its biological effects.³ There are a number of methods developed to assess the body composition, including dual-energy X-ray absorptiometry (DEXA), BIA, densitometry, isotope dilution (hydrometry), and magnetic resonance imaging.²⁶ Currently, DEXA is the gold standard approach to estimate body composition, but it can hardly be used to make measurements on large samples. Bioelectric impedance analysis is relatively less expensive and easier to use, and therefore suitable for epidemiological studies.²⁷ In our present study, whole-body composition was measured by a commercial analyzer (TBF-418B; Tanita Corp.) based on BIA technique. Using this quick and noninvasive method, a variety of detailed parameters concerning body composition can be calculated and are reliable to assess adiposity in children.^28,29 By using this approach, we found the association of each specific body component with RVCs. For instance, positive relationship between FMI and CRVE was observed in adolescents, indicating an explicit correlation between body fat increase and microvascular changes. Our study also showed an inverse correlation between the BWP and CRVE, which cannot be revealed by BMI measurement. To our best knowledge, the relationship between body composition and RVC has not been described previously in children or adults. 

Several factors may account for the association between the increased adiposity and retinal vascular diameters. First, obese individuals have greater total blood volume as compared with nonobese individuals. Blood vessels may thus be widened adaptively to accommodate the increased volume of blood flow. Second, the vascular tone of arteries, venules, and blood flow in the human retina are mainly regulated by nitric oxide (NO).³⁰ Leptin, which is increased in obese individuals, can alter NO synthesis and lead to vasodilation.³¹ Third, in obese subjects, preinflammatory factors, such as TNF-α, expressed in adipose tissue could play a direct role in venular widening.³² 

Major strengths of the current study are the objective measurement of body composition and the relatively large sample size. Additionally, subjects enrolled were young healthy children and free of known systemic (e.g., type 1 diabetes) or ocular disease. Nevertheless, there are also some limitations to our study. First, although BIA measuring for body composition is noninvasive and widely used, its interpretation may be population- and race-specific.³³ Second, several potentially relevant factors (e.g., birth size³⁴ and corneal resistance factor¹⁷) were not collected, so they were not included in the multiple linear regression model. Indeed, a previous study showed the relationship of greater BMI and narrower retinal arteriolar caliber in 6-year-old children,¹¹ but such relationship was not observed in our study (Supplementary Table S1). Third, selection bias may exist because the subjects are from a population-based twin registry. Although the distributions of systemic and ocular characteristics well resemble those reported in population-based studies,³⁵ one should remain cautious of generalizing our results to the general population. 

In conclusion, this study documents a relationship between various body fat measurements and RVC in children and adolescents. Our findings are in agreement with existing evidence that pediatric obesity has an effect on the microvascular system. Whether control of obesity or normalization of body composition actually leads to beneficial changes in microvasculature remains to be investigated in longitudinal studies or even clinical trials. 

Acknowledgments

Supported by the National Natural Science Foundation of China (81125007) and Basic Research Funds of the State Key Laboratory. The authors alone are responsible for the content and writing of the paper. 

Disclosure: W. Xiao, None; W. Gong, None; Q. Chen, None; X. Ding, None; B. Chang, None; M. He, None