A national survey of iodine nutrition in children aged 3–6 years in China and its relationship with children's physical growth

Abstract

Iodine, an essential trace element for the human body, plays a pivotal role in sustaining health. Malnutrition has emerged as a pressing public health concern, posing a significant threat to human well‐being. Iodine deficiency poses a substantial threat to the development of children, potentially leading to neurological developmental disorders and mental retardation. Conversely, excessive iodine intake can result in structural and functional abnormalities in the thyroid gland. In this study, we selected children aged 3–6 years through a stratified cluster sampling approach in six regions across China to explore the correlation between iodine nutrition and their physical growth. A total of 5920 preschool children participated in this study, with a median urinary iodine concentration (UIC) of 177.33 [107.06, 269.92] μg/L. Among these children, 250 (4.2%) exhibited stunting, 180 (3.0%) were underweight, 198 (3.3%) experienced wasting, 787 (3.3%) were overweight and 414 (7.0%) were classified as obese. The multivariate linear regression revealed that UIC exhibited a positive correlation with body mass index z‐Score (BMIZ) in overweight children (β = 0.038; 95% CI: 0.001, 0.075). In normally growing children, the associations between UIC and height‐for‐age z‐score, weight‐for‐age z‐score and BMIZ displayed nonlinear patterns. Our findings suggest that iodine nutrition is adequate for Chinese children aged 3–6 years. Furthermore, iodine nutrition is intricately linked to the growth and development of these children. Consequently, it is imperative to implement decisive measures to prevent both iodine deficiency and excess.

This study highlights the iodine nutritional status among Chinese children aged 3–6 years, revealing that while levels are generally adequate. Our findings stress the importance of tailored iodine supplementation to support optimal health and development in preschoolers.

Key messages

• This paper is the first large‐scale nationwide epidemiological survey of iodine nutrition in preschool children in China.

• The median urinary iodine of preschool children in China was 177.33 [107.06, 269.92] μg/L, indicating adequate iodine nutrition.

• The study suggests that there may be a nonlinear correlation between median urinary iodine and physical growth indicators, such as height‐for‐age z‐score, in preschool children, with gender differences.

1. INTRODUCTION

Malnutrition poses a significant threat to human health and has emerged as a major public health concern. The World Health Organization (WHO) estimated that in 2018, 52 million children under the age of 5 were affected by wasting, 155 million experienced stunting and 41 million were either overweight or obese globally (World Health Organization, 2012). China is the world's most populous country, with a population of approximately 1.4 billion, accounting for one‐fifth of the world's children (China(CHN)‐Demographics, 2021). Although China's rapid socioeconomic development, urbanization and modernization have greatly improved the nutritional status of children in recent decades, in 2010, China still had a stunting rate of 9.9% and an underweight rate of 3.6% for children under 5 years of age (Ayling et al., 2023). What's more, over the past few decades, overweight has increased by 88.9% and obesity by a factor of 2.14 among children aged 1–4 years (Yang et al., 2020). The preschool years represent a crucial phase of growth and development, with conditions such as underweight, overweight or obesity during childhood strongly linked to numerous adverse health outcomes across the lifespan. The burden of undernutrition carries profound and enduring developmental, economic, social and medical implications for individuals, families, communities and nations (Abarca‐Gómez et al., 2017; Z. Li et al., 2020).

Iodine, as an essential micronutrient, plays a pivotal role in enabling the body to produce critical enzymes, hormones and other substances necessary for normal growth and development (Black et al., 2013). Iodine deficiency poses a substantial threat to the development of children, potentially leading to neurological developmental disorders and mental retardation (Zimmermann et al., 2008). Conversely, excessive iodine intake can result in structural and functional abnormalities in the thyroid gland, including conditions like hyperthyroidism and autoimmune thyroid disorders (Farebrother et al., 2019). The median urinary iodine concentration (UIC) serves as the primary indicator for evaluating iodine nutrition within a population (Zimmermann & Andersson, 2021). Since approximately 90% of ingested iodine is excreted in urine, urinary iodine levels serve as an effective indicator of an individual's iodine intake. Iodine deficiency has been recognized as a serious public health problem in China since the 1930s. Data collected from the 1960s to the 1990s showed that all provinces in China had varying degrees of iodine deficiency (Wang et al., 1997). After 1995, China began a salt iodization programme, which reduced the prevalence of iodine deficiency in many populations globally, but 30% of the current global population is still at risk of iodine deficiency (Hatch‐McChesney & Lieberman, 2022). What's more, iodine nutritional surveillance in China has focussed mainly on schoolchildren aged 8–10 years, with relatively few surveys involving preschoolers.

In this study, we investigated the iodine nutrition of preschool children. This was accomplished through stratified random sampling of children aged 3–6 years across various regions in China, with a specific focus on understanding how iodine nutrition relates to physical growth. This study offers a more robust scientific foundation for future iodine nutrition monitoring efforts and explores whether the physical growth status of children should be considered as a factor in these assessments.

2. MATERIALS AND METHODS

2.1. Study population

This cross‐sectional study employed a multistage stratified block cluster random sampling method, as illustrated in Figure 1. To outline the process, first, one province was randomly chosen as the survey site within each geographical region. Second, within each selected province, towns and villages from large cities, small‐ and medium‐sized cities, as well as rural areas, were selected using the probabilistic proportional to size (PPS) sampling method, which was based on population size. Third, within each selected district or village, children from 1 to 2 communities were randomly chosen using a simple random sampling (SRS) approach. The survey was conducted over the period from June 2013 to May 2014, and the study's target population consisted of preschool children aged 3–6 years within the surveyed area. Inclusion criteria required a minimum of 2 years of continuous residence in the area, while exclusion criteria encompassed children with thyroid disorders (e.g., endemic goitre, hyperthyroidism, thyroiditis, etc.) or other conditions that could potentially impact UIC.

Figure 1.

Flowchart for multistage stratified block cluster random sampling.

2.2. Sample size calculation

The sample size was determined employing the formula $n={\left(\frac{{\mu }_{\alpha }\times \sigma }{\delta }\right)}^2$, wherein δ represents allowable error, and δ = −μ, with a 95% confidence interval and μ _α  = 1.96, was adopted to ensure precision. As per our preceding work, the median UIC among school‐age children in Shanghai in 2010 was 199.75 μg/L. Assigning σ to 200 μg/L and an allowable error of 10 μg/L (relative error of approximately 5%), while considering a missed follow‐up rate of 15%, the projected sample size is determined to be 5302. Additionally, considering the financial considerations associated with this programme, the final sample size has been established as 6271 children, ranging in age from 3 to 6 years.

2.3. Sample collection

The measurement of the median UIC of a random urine sample from a population is the preferred method of determining the iodine status of a population and is usually expressed as µg/L (Eastman, 2012). At the designated community health centres, random midstream urine samples were obtained from the children. These samples were preserved cryogenically and promptly transported to the laboratory within 24 h of collection. Upon arrival at the laboratory, the urine samples were dispensed into centrifuge tubes and stored in a −80°C refrigerator for further analysis. Height and weight were measured uniformly by staff according to WHO standards using an accurate height and weight measuring device (Seca 779; Vogel & Halke), with two consecutive measurements and readings, accurate to 0.1 cm or 0.1 kg. The body mass index (BMI) was calculated as the square of the height (in metres) divided by the weight (in kilograms) (World Health Organization, 2024).

Five anthropometric failure outcomes were constructed based on the WHO child growth standards, in which children aged 3–5 years were based on the 2006 Growth Standards (World Health Organization, 2023) and children aged 5–6 years were based on the 2007 Growth Reference (de Onis et al., 2007): stunting (height‐for‐age z score less than −2 standard deviations [SDs]), underweight (weight‐for‐age z score less than −2 SDs), wasting (weight‐for‐height z score less than −2 SDs), overweight (Body Mass Index Z‐Score between +1 SDs and +2 SDs) and obesity (BMIZ exceeding +2 SDs).

The questionnaire was administered by uniformly trained personnel and was completed by the child's guardian. It collected basic information and demographic characteristics of the surveyed children. These demographic characteristics encompassed the child's age, gender, ethnicity, the source of drinking water, geographic location, degree of urbanization and exposure to passive smoking. Passive smoking was defined as a child's inhalation of smoke exhaled by a smoker for more than 1 day in a week, involving a minimum of 15 min of exposure per day.

2.4. Assessment of UIC

The concentration of iodine in urine was quantified using the standard addition method (SAM) with an inductively coupled plasma mass spectrometer (Agilent ICP‐MS/MS, Model: 8900). The urine samples were diluted with a 0.25% tetramethylammonium hydroxide solution. Ultrapure water with a resistance of 18.2 MΩ/cm was consistently utilized throughout the analysis.

The WHO's criteria for evaluating iodine status in children were adhered to (World Health Organization, 2007). Accordingly, MUI <100 μg/L was defined as iodine deficient (DI), MUI between 100 and 199 μg/L as adequate iodine (AI), 200–299 μg/L as more than adequate iodine (MAI) and MUI ≥300 μg/L as excessive iodine (EI).

2.5. Quality control

In the determination of UIC, iodine standard samples, reagent blanks and sample blanks were introduced into each sample batch to ensure quality control (Huang et al., 2023). During the testing process, the relative deviation of the iodine standard sample was maintained within 5% of the specified standard value. The standard curve exhibited a high correlation coefficient (r) exceeding 0.999. The limit of detection (LOD) for urinary iodine was established at 0.089 µg/L. It is worth noting that all urinary iodine values from the collected samples exceeded the LOD, confirming the reliability of the data.

2.6. Statistic analysis

For normally distributed data, continuous variables were presented as mean ± SD, while categorical variables were expressed as frequency (percentage). However, for data with a skewed distribution, concentration trends were represented as median [interquartile range]. It is worth noting that the concentration of iodine in urine exhibited a skewed distribution. To investigate the relationship between iodine nutrition and the physical growth of preschool children, multiple linear regression models were employed. Covariates used in these models were derived from the basic information of preschool children. For the exploration of any potential nonlinear relationships between UIC (ln‐transformed) and physical growth, unrestricted cubic splines were utilized. The chosen covariates for this analysis matched those used in the multiple linear regression model. The knots in the spline were strategically positioned at the 5th, 27.5th, 50th, 72.5th and 95th percentiles for a comprehensive examination of any potential nonlinear associations.

Children's z‐scores were computed using three different methods: R, specifically the anthro package (The Comprehensive R Archive Network, n.d.) and the anthroplus package (The Comprehensive R Archive, n.d.) and the official WHO website software (World Health Organization, n.d.). Statistical analysis of the data was conducted using R 4.2.1 (Lucent Technologies). A two‐sided test was applied in this study, with a significance level set at p < 0.05 to determine statistically significant differences.

2.7. Ethics statement

This research was approved by the Medical Ethics Committee of Xinhua Hospital affiliated with Shanghai Jiao Tong University School of Medicine and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from children's parents recruited in the study.

3. RESULT

3.1. Demographic characteristics

Out of the initial 6271 children who met the inclusion criteria, 351 (5.6%) were excluded due to missing data (including cases of absence, refusal and other reasons) or implausible anthropometric measurements. Consequently, the final study cohort comprised 5920 children aged 3–6 years, and their detailed characteristics are summarized in Table 1. The average age of the preschool children was 58.01 ± 12.15 months, with 3200 (54.1%) being boys. Among the participants, 892 (15.1%) of them belonged to minority ethnic groups and 2031 (34.3%) hailed from rural areas. The children were recruited from various regions across China, including eastern, central, southern, southwestern and northwestern areas, with 1354, 983, 760, 906, 1200 and 717 children, respectively. Regarding water sources, 61.8% of the children consumed tap water, and 55.0% had experienced passive smoking. The cohort exhibited a mean height‐for‐age z‐score of 0.01 ± 1.25, a mean weight‐for‐age z‐score of 0.08 ± 1.19 and a mean BMI z‐score of 0.14 ± 1.40. Specifically, 250 (4.2%) of the children were categorized as stunted, 180 (3.0%) as underweight, 198 (3.3%) as wasted, 787 (13.3%) as overweight and 414 (7.0%) as obese.

Table 1.

Demographic characteristics and information of the study population.

Variables	Mean ± SD/n (%)	N
Age	58.01 ± 12.15	5920
Gender		5920
Boy	3200 (54.1)
Girl	2720 (45.9)
Ethnic group		5920
Han	5028 (84.9)
Minority	892 (15.1)
Position		5920
East China	1354 (22.9)
Central China	983 (16.6)
North China	760 (12.8)
South China	906 (15.3)
Southwest China	1200 (20.3)
Northwest China	717 (12.1)
District		5920
Rural areas	2031 (34.3)
Small‐ and medium‐sized urban areas	1794 (30.3)
Large urban areas	2095 (35.4)
Water source		5920
Tap water	3657 (61.8)
Groundwater (deep well water)	644 (10.9)
Surface water (river/lake/shallow well water)	80 (1.3)
Purified water	1539 (26.0)
Passive smoke		5920
Yes	3255 (55.0)
No	2665 (45.0)
Height‐for‐age z‐score (HAZ)	0.01 ± 1.25	5920
Weight‐for‐age z‐score (WAZ)	0.08 ± 1.19	5920
BMI for z‐score (BMIZ)	0.14 ± 1.40	5920
Stunting	−2.69 ± 0.82	250 (4.2%)
Underweight	−2.48 ± 0.49	180 (3.0%)
Wasting	−2.79 ± 0.95	198 (3.3%)
Overweight	1.41 ± 0.27	787 (13.3%)
Obesity	3.19 ± 1.99	414 (7.0%)

3.2. UIC in preschool children

UIC in preschool children exhibited a skewed distribution, and all children had detectable levels of iodine, yielding a detection rate of 100%. Table 2 presents the median UIC, which were as follows: 177.33 [107.06, 269.92] μg/L for all children, 192.44 [118.11, 289.70] μg/L for boys and 167.93 [94.49, 244.93] μg/L for girls. It's worth noting that the median UIC in boys was significantly higher than that in girls (p < 0.001).

Table 2.

Concentrations of iodine in urine.

Gender	N	Mean	Min	P5	P25	P50	P75	P95	Max
Boy	3200	220.40	30.12	50.63	118.11	192.44	289.70	482.99	796.80
Girl	2720	188.69	30.01	47.02	94.49	167.93	244.93	440.32	770.03
Total	5920	205.83	30.01	48.76	107.06	177.33	269.92	468.01	796.80

3.3. Associations of UIC with physical growth of preschool children

We employed multiple linear regression analysis to investigate the impact of UIC (ln‐transformed) on the physical growth of preschool children. As presented in Table 3, the results revealed that children's UIC was positively associated with BMIZ for overweight (β = 0.038; 95% CI: 0.001, 0.075). Meanwhile, we conducted sensitivity analyses. These analyses demonstrated that UIC was positively linked to overweight in boys (β = 0.060; 95% CI: 0.007, 0.112) but not in girls (β = 0.029; 95% CI: −0.032, 0.091). Furthermore, UIC exhibited a negative association with height‐for‐age in girls (β = −0.083; 95% CI: −0.161, −0.004), while the results were not statistically significant in boys. Importantly, there were no discernible sex‐based differences in the relationship between UIC and preschool children's weight‐for‐age, BMIZ, stunting, underweight, wasting and obesity.

Table 3.

Associations between iodine in urine (ln‐transformed) and physical growth evaluation indicators from multiple linear regression.

Physical growth evaluation indicators	Boy		Girl		Total
	β (95% CI)	p valuea	β (95% CI)	p value a	β (95% CI)	p value a
HAZb	−0.025 (−0.109, 0.060)	0.564	−0.083 (−0.161, −0.004)	0.039	0.015 (−0.072, 0.043)	0.619
WAZc	−0.012 (−0.092, 0.068)	0.773	−0.067 (−0.140, 0.005)	0.069	0.012 (−0.043, 0.066)	0.674
BMIZd	0.053 (−0.049, 0.155)	0.310	−0.050 (−0.137, 0.038)	0.268	0.044 (−0.024, 0.111)	0.202
Stunting b	−0.021 (−0.223, 0.181)	0.838	−0.156 (−0.383, 0.072)	0.176	0.015 (−0.189, 0.218)	0.888
Underweight c	0.013 (−0.231, 0.258)	0.914	−0.115 (−0.508, 0.279)	0.563	−0.082 (−0.232, 0.068)	0.281
Wasting d	0.047 (−0.313, 0.407)	0.795	−0.335 (−0.722, 0.051)	0.088	−0.087 (−0.342, 0.168)	0.503
Overweight d	0.060 (0.007, 0.112)	0.026	0.029 (−0.032, 0.091)	0.345	0.038 (0.001, 0.075)	0.046
Obesity d	0.144 (−0.361, 0.648)	0.575	−0.124 (−0.755, 0.507)	0.698	0.094 (−0.297, 0.484)	0.638

Note: Bold values denote less than or equal to 0.05.

^a The p value was estimated from the multiple linear regression models according to the median value of each quartile of elements as a continuous variable.

^b The model was adjusted for age, creatinine, gender, nation, position, district, passive smoking, water source, and height of parents.

^c The model was adjusted for age, creatinine, gender, nation, position, district, passive smoking, water source, and weight of parents.

^d The model was adjusted for age, creatinine, gender, nation, position, district, passive smoking, water source, and BMI of parents.

In the quantile‐treated linear regression model (as shown in Table 4), UIC (ln‐transformed) exhibited a negative correlation with age‐specific height (p = 0.037). Specifically, the second, third and fourth percentiles showed changes in β values of −0.067 (95% CI: −0.154, 0.020), −0.062 (95% CI: −0.154, 0.028) and −0.132 (95% CI: −0.239, −0.025), respectively. After stratifying the analysis by sex, it was observed that the association between urinary iodine levels and height‐for‐age was significantly negative in girls (p = 0.050), while no such association was observed in boys. Additionally, UIC demonstrated a positive association with overweight (p = 0.013), with β values increasing by 0.027 (95% CI: −0.030, 0.084), 0.064 (95% CI: 0.002, 0.126) and 0.085 (95% CI: 0.014, 0.156) at the second, third and fourth percentiles, respectively.

Table 4.

Quantile regression for associations between urine iodine concentrations and physical growth indicators (95% confidence intervals).

	Quartiles of urine iodine concentrations (ln‐transformed, μg/L)
Outcomes	Q1	Q2	Q3	Q4	p for trend a
HAZ b
Boy	Ref	−0.097 (−0.222, 0.028)	−0.051 (−0.188, 0.086)	−0.157 (−0.311, −0.003)	0.074
Girl	Ref	−0.067 (−0.170, 0.036)	−0.029 (−0.154, 0.096)	−0.182 (−0.332, −0.032)	0.050
Total	Ref	−0.067 (−0.154, 0.020)	−0.062 (−0.154, 0.028)	−0.132 (−0.239, −0.025)	0.037
WAZ c
Boy	Ref	−0.051 (−0.170, 0.068)	−0.047 (−0.178, 0.084)	−0.109 (−0.256, 0.038)	0.171
Girl	Ref	−0.052 (−0.148, 0.044)	−0.099 (−0.215, 0.017)	−0.217 (−0.266, 0.012)	0.053
Total	Ref	−0.044 (−0.126, 0.038)	−0.011 (−0.114, 0.092)	−0.056 (−0.364, 0.252)	0.169
BMIZ d
Boy	Ref	0.101 (−0.051, 0.253)	0.014 (−0.153, 0.181)	0.043 (−0.144, 0.230)	0.732
Girl	Ref	−0.037 (−0.153, 0.079)	−0.137 (−0.277, 0.003)	−0.069 (−0.238, 0.100)	0.204
Total	Ref	−0.061 (−0.147, 0.025)	−0.019 (−0.126, 0.088)	0.014 (−0.324, 0.352)	0.724
Stunting b
Boy	Ref	−0.102 (−0.495, 0.291)	0.019 (−0.409, 0.447)	−0.012 (−0.462, 0.438)	0.992
Girl	Ref	−0.007 (−0.574, 0.560)	0.134 (−0.459, 0.727)	0.049 (−0.680, 0.778)	0.821
Total	Ref	−0.069 (−0.170, 0.032)	0.059 (−0.067, 0.185)	0.006 (−0.374, 0.386)	0.989
Underweight c
Boy	Ref	0.043 (−0.289, 0.375)	0.351 (−0.064, 0.766)	0.063 (−0.341, 0.467)	0.854
Girl	Ref	−0.099 (−0.417, 0.219)	0.093 (−0.231, 0.417)	−0.166 (−0.527, 0.195)	0.635
Total	Ref	0.017 (−0.214, 0.248)	0.021 (−0.237, 0.279)	−0.072 (−0.352, 0.208)	0.809
Wasting d
Boy	Ref	0.325 (−0.289, 0.939)	0.255 (−0.410, 0.920)	0.118 (−0.594, 0.830)	0.783
Girl	Ref	−0.163 (−0.614, 0.288)	−0.402 (−0.906, 0.102)	−0.480 (−1.154, 0.194)	0.117
Total	Ref	−0.020 (−0.411, 0.371)	−0.080 (−0.520, 0.360)	−0.167 (−0.651, 0.317)	0.440
Overweight d
Boy	Ref	0.074 (−0.003, 0.151)	0.073 (−0.009, 0.155)	0.084 (−0.009, 0.177)	0.067
Girl	Ref	0.000 (−0.084, 0.084)	0.065 (−0.028, 0.158)	0.061 (−0.051, 0.173)	0.188
Total	Ref	0.027 (−0.030, 0.084)	0.064 (0.002, 0.126)	0.085 (0.014, 0.156)	0.013
Obesity d
Boy	Ref	0.444 (−0.296, 1.184)	0.217 (−0.589, 1.023)	0.344 (−0.572, 1.260)	0.488
Girl	Ref	0.008 (−0.870, 0.886)	−0.717 (−1.636, 0.202)	−0.145 (−1.280, 0.990)	0.587
Total	Ref	0.195 (−0.367, 0.757)	0.012 (−0.610, 0.634)	0.305 (−0.391, 1.001)	0.428

Note: Bold values denote less than or equal to 0.05.

^a The p for trend was estimated from the multiple linear regression models according to the median value of each quartile of elements as a continuous variable.

^b The model was adjusted for age, creatinine, gender, nation, position, district, passive smoking, water source, and height of parents.

^c The model was adjusted for age, creatinine, gender, nation, position, district, passive smoking, water source, and weight of parents.

^d The model was adjusted for age, creatinine, gender, nation, position, district, passive smoking, water source, and BMI of parents.

Restricted cubic splines (RCS) were employed to investigate the relationship between UIC and the physical growth of children aged 3–6 years in a multivariate model (as depicted in Figure 2). In the context of this nonlinear relationship, it was observed that children's height‐for‐age z‐score initially increased as UIC increased but subsequently decreased with further increases in UIC (p = 0.009). Upon stratifying the analysis by sex (as demonstrated in Supporting Information: Figures 1 and 2), the nonlinear association between UIC and height‐for‐age z‐score remained statistically significant in boys (p = 0.006) but not in girls. Furthermore, it was noted that underweight z‐scores exhibited a tendency to rise with increasing UIC in girls. However, beyond a certain threshold of UIC, underweight z‐scores decreased as UIC continued to increase (p = 0.009).

Figure 2.

Nonlinear associations between iodine concentrations in urine (ln‐transformed) and physical growth evaluation indicators. Nonlinear associations were presented by the restricted cubic splines and adjusted by the covariates. Shading in the plots indicated the confidence interval (95% CI). The knots in the plots were set at 5th, 27.5th, 50th, 72.5th and 95th percentile, respectively.

4. DISCUSSION

Childhood iodine malnutrition continues to be a significant public health concern worldwide. However, the findings from this study indicate that Chinese children displayed adequate iodine nutrition, with low prevalence of wasting, underweight and stunting. Nonetheless, it is notable that there is an increasing proportion of children who are classified as overweight and obese. This study underscored the close relationship between iodine nutrition and physical growth, as evidenced by indicators such as height‐for‐age z‐score (HAZ), weight‐for‐age z‐score (WAZ) and BMIz in preschool children. These results shed light on the importance of monitoring and addressing nutrition, particularly in the context of an emerging trend of overweight and obesity in this demographic.

Children's physical growth is an ongoing and dynamic process, and preschoolers, typically aged between 3 and 7 years, occupy a critical stage in their growth and development, constituting a pivotal link in the overall trajectory of lifelong health. The physical growth of preschool children serves as a valuable reflection of the development of social and economic groups to a certain extent and stands as a crucial indicator of health care quality and population well‐being (Tanner, 1992). Iodine, a trace element vital for human well‐being, must be acquired through dietary sources or drinking water. In our current study, we analysed urine samples collected from a cohort of 5920 children aged 3–6 years, with a primary focus on exploring the impact of iodine nutrition on the physical growth of preschool children.

To paint a more robust and compelling picture of the influence of iodine nutrition on the physical growth of preschool children, our analysis incorporated not only the impacts of dependent variables, including age, gender, ethnicity and genetic factors (such as parental height, weight and BMI) but also considered factors like creatinine, geographic location and drinking water source. These confounding factors have a notable impact on the independent variables, enhancing the depth and comprehensiveness of our examination. UIC is highly susceptible to urine dilution in children. However, the urinary creatinine correction minimizes the variation caused by differences in subjects' urine volume and dilution in a well‐nourished iodine population and is highly informative in describing the iodine nutritional status of a population (Mulder et al., 2020). There are also differences in iodine status in populations with different geographical locations and drinking water sources (Delange & Bürgi, 1989; Hou et al., 2023). Therefore, this study speaks of these factors as covariate models.

In our current study, we found that the median UIC in Chinese preschool children was 177.33 μg/L and was higher in boys, which is consistent with previous studies and may be due to different metabolism and physiology between the sexes (Fuse et al., 2022; Yan et al., 2023). A study conducted in rural areas of China, specifically among preschool children, reported a median UIC of 181 μg/L (Zou et al., 2014). In comparison, larger epidemiological surveys in China involving school‐age children (SAC) have shown median urinary iodine levels around 197 μg/L (Y. Li et al., 2020; Shan et al., 2016). The 2014 National Survey of Iodine Deficiency Disorders in China also indicated an average iodine nutritional level of 197.9 μg/L among school‐age children (Chao et al., 2016). Among other Asian countries, a national study in Japan showed a median urinary iodine level of 269 μg/L in Japanese schoolchildren (Fuse et al., 2022), while research in Thailand revealed that preschoolers had the median UIC of 262 μg/L (Zimmermann et al., 2016). In South Korea, a cross‐sectional study found that the median UIC in Korean preschoolers was notably high at 438.8 μg/L (Lee et al., 2014). In contrast, a study conducted in Cambodia reported a median UIC of only 72 μg/L among their preschool children (Laillou et al., 2016). In a comprehensive context, the median UIC in Chinese preschool children aligns with the results from studies involving Chinese children. Furthermore, when compared with other Asian countries, iodine nutrition in Chinese preschool children appears to be within the optimal range, signifying a positive nutritional status in this population.

In our present study, the prevalence rates of stunting, underweight, wasting, overweight and obesity among children aged 3–6 years were 4.2%, 3.0%, 3.3%, 13.3% and 7.0%, respectively. To provide context, let's compare these findings with results from previous studies in China. According to the 2013 Chinese population census, the rates of stunting, underweight and wasting among children under 5 years of age were reported to be 8.1%, 2.4% and 1.9%, respectively (Yu et al., 2016). A nationwide cohort social study conducted in China in 2013 found overweight and obesity rates of 8.4% and 3.1%, respectively, in children aged 0–5 years (Pan et al., 2021). Additionally, a nationwide cohort social study in China in 2014 reported a stunting rate of 1.4% in school‐aged children, with a high overweight rate of 20.4% (Song et al., 2019; Yao et al., 2022). In 2015, a cross‐sectional study of Chinese children under 7 years of age presented prevalence rates of overweight and obesity at 12.6% and 4.3%, respectively (Zong et al., 2023). In 2016, a longitudinal survey in China displayed rates of stunting, underweight, wasting and overweight or obesity in children under 7 years of age as 0.7%, 0.6%, 1.2% and 7.6%, respectively (Zhang et al., 2021). Compared with other studies in China, the incidence of stunting, underweight and wasting among Chinese preschoolers in this survey remained low and demonstrated a decreasing trend. In contrast, the incidence of overweight and obesity among preschoolers was at an intermediate level and displayed a gradual upward trend. Dietary habits in China have undergone significant transformation, marked by a rise in the intake of animal‐based foods, refined grains and heavily processed foods rich in sugar and fat. Concurrently, there has been a universal decline in physical activity and an uptick in sedentary lifestyles (Zhu et al., 2019). The impact of these dietary changes and physical inactivity is closely interlinked with other individual risk factors, such as genetic predispositions, psychosocial elements, obesogens and exposures during fetal development and early childhood. This complex web of factors has led to an escalating prevalence of overweight and obesity among children, alongside a reduction in cases of wasting and low body weight.

Our findings suggest a nonlinear correlation between height‐for‐age and UIC in preschool children, with height‐for‐age z‐scores reaching their peak when UIC are at an appropriate level. Furthermore, linear regression analysis reveals that iodine nutrition is positively associated with overweight in children. Previous research has reported significant differences in UIC between overweight and obese children and their normal weight counterparts (De Angelis et al., 2020), although there is no consensus on these findings. For instance, a Chinese cross‐sectional survey suggested that median urinary iodine was lower in overweight schoolchildren (Shan et al., 2021), while a report from Mexico indicated no significant difference in median UIC between overweight and obese children compared with normal weight children (Gonzalez‐Nunez et al., 2021). On the other hand, a Brazilian survey suggested that overweight/obesity might act as a protective factor against excessive iodine intake in children (Campos Rde et al., 2016). Meanwhile, another Brazilian study investigating children aged 6–14 years demonstrated that lower BMI was associated with lower median UIC. These findings illustrate the complexity and variability of the relationship between iodine nutrition and weight status in children (Cesar et al., 2020).

Iodine plays a pivotal role in the synthesis of thyroid hormones, which are indispensable for normal growth and development. The relationship between iodine intake and physical growth is multifaceted. On the one hand, both insufficient and excessive iodine intake can disrupt the balance of thyroid hormones, leading to a decrease in the body's thyroid hormone levels. This, in turn, can result in a slowdown of the basal metabolic rate and an increase in energy storage, favouring the accumulation of fat and thus elevating the risk of obesity (Bianco & Kim, 2006; Luongo et al., 2019). On the other hand, an excess of thyroxine, a thyroid hormone, can modulate the activity of fatty acid synthase, inhibiting fatty acid synthesis and consequently reducing fat accumulation (García‐Solís et al., 2018). It also promotes fatty acid oxidation, making fat a more efficient energy source and, therefore, reducing fat storage and controlling body weight (Miro et al., 2023). Additionally, thyroxine promotes lipolysis, which involves the breakdown of fat reserves into fatty acids and glycerol for energy utilization, further reducing body fat mass (de Vries et al., 2015). It's important to acknowledge that the relationship between thyroid hormones and physical growth is intricate and not solely influenced by thyroid hormone levels. It is also shaped by a multitude of factors, including individual genetics, lifestyle choices and dietary habits, which collectively contribute to this complexity.

This study boasted several notable strengths. First, it represented one of the few large‐scale nationwide cross‐sectional surveys conducted in China that specifically target the crucial age group of 3–6 years. Second, the study implemented stringent inclusion and exclusion criteria, with the comprehensive collection of basic information, ensuring a robust data set. Furthermore, the study effectively elucidated the influence of iodine nutrition on children's physical growth, employing a multifactorial model and integrating sensitivity analysis to mitigate the potential impact of confounding factors, thereby enhancing the stability of the model. Nonetheless, there are certain limitations in our study. First, it is challenging to directly establish the causal effect of iodine nutrition on children's physical growth in the context of this cross‐sectional investigation, as children's physical growth is influenced by a multitude of factors, including dietary habits, sociocultural elements and various other variables. Consequently, clinical trials designed with a high degree of rigour and control might offer a more effective means of unravelling the intricate relationship between iodine nutrition and physical growth in preschool children. In addition, this study did not collect 24‐h urine volumes from children to calculate 24‐h excretion (μg/day), but variations in hydration among individuals generally even out in a large number of samples so that the median UI in spot samples correlates well with that from 24‐h samples (Zimmermann, 2020).

5. CONCLUSION

The findings of this study indicate that iodine nutrition in Chinese children aged 3–6 years are generally adequate. Moreover, preschool children iodine nutrition and preschool children's physical growth is closely associated with the age of the children's height, which emphasizes the need for adherence to scientifically backed iodine supplementation practices for preschool‐aged children and highlights the critical role of appropriate iodine intake in their overall health and development. Furthermore, these insights suggest that our next course of action should be to offer personalized iodine nutritional advice to individuals.

AUTHOR CONTRIBUTIONS

Chonghuai Yan conceptualized and designed the research. Jing Li performed the research, analysed the data and drafted the paper. Junxia Liu, Xiaoli Shen and Yuqing Wang provided input on the design and contributed to the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

Supporting information

Supplementary information.

Li, J. , Liu, J.‐X. , Shen, X.‐L. , Wang, Y.‐Q. , & Yan, C.‐H. (2024). A national survey of iodine nutrition in children aged 3–6 years in China and its relationship with children's physical growth. Maternal & Child Nutrition, 20, e13685. 10.1111/mcn.13685

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are not openly available due to the inclusion of information that could compromise the subject's privacy but are available from the corresponding author on reasonable request.