Childhood dietary patterns and body composition at age 6 years: the Children of SCOPE study

Abstract

Dietary patterns describe the quantity, variety, or combination of different foods and beverages in a diet and the frequency of habitual consumption. Better understanding of childhood dietary patterns and antenatal influences could inform intervention strategies to prevent childhood obesity. We derived empirical dietary patterns in 1142 children (average age 6.0 (0.2) years) in Auckland, New Zealand whose mothers had participated in the Screening for Pregnancy Endpoints (SCOPE) cohort study and explored associations with measures of body composition. Participants (Children of SCOPE) had their diet assessed by food frequency questionnaire (FFQ) and empirical dietary patterns were extracted using factor analysis. Three distinct dietary patterns were identified; ‘Healthy’, ‘Traditional’ and ‘Junk’. Associations between dietary patterns and measures of childhood body composition (waist, hip, arm circumferences, body mass index (BMI), bioelectrical impedance analysis (BIA) derived body fat percentage, and sum of skinfold thicknesses (SST)) were assessed by linear regression, with adjustment for maternal influences. Children who had higher ‘Junk’ dietary pattern scores had 0.24cm greater arm (0.08 SD (95%CI 0.04, 0.13)) and 0.44cm hip (0.05 SD (95% CI 0.01, 0.10)) circumferences, 1.13cm greater SST (0.07 SD (95%CI 0.03, 0.12)) and were more likely to be obese (OR=1.74 (95%CI 1.07, 2.82)); those with higher ‘Healthy’ pattern scores were less likely to be obese (OR=0.62 (95%CI 0.39, 1.00)). In a large mother-child cohort, a dietary pattern characterised by high sugar and fat foods was associated with greater adiposity and obesity risk in children aged 6 years, while a ‘Healthy’ dietary pattern offered some protection against obesity. Targeting unhealthy dietary patterns could inform public health strategies to reduce the prevalence of childhood obesity.

Introduction

Childhood obesity impacts on both health in childhood, and adult life ⁽¹⁾. Global estimates from the World Health Organization indicate that 18% of children and adolescents aged 5-19 years were overweight or obese in 2016 ⁽²⁾. Diet is a key determinant of childhood obesity ⁽³⁾; as dietary habits established in early childhood may track into adulthood ^(4,5), interventions which change dietary intake early in life have the potential to improve lifelong health. Effective intervention strategies will depend on better understanding of the specific dietary elements which contribute to obesity risk.

The assessment of dietary patterns provides a practical alternative to the more conventional measurement of isolated nutrients and dietary components, the limitations of which are appreciated ⁽⁶⁾. Dietary patterns can be derived by one of two approaches; theoretically, in which dietary variables are grouped according to a priori criteria of nutritional health, e.g. a dietary index, or empirically, where dietary variables are condensed into clusters of variables using statistical modelling and examined a posteriori ⁽⁷⁾. This study used the empirical method as an assessment of total diet as it is more likely to be informative when addressing dietary risk factors for childhood obesity than diet indexes which focus on selected foods or nutrients.

Despite being widely explored in adult cohorts ⁽⁸⁾, relationships between empirical dietary patterns and childhood obesity risk have seldom been studied ^(9,10), especially in the context of contemporary mother-child cohorts. Ideally, associations between childhood dietary patterns and obesity are best examined in studies with information on the potentially confounding effects of maternal obesity, gestational diabetes and the mother’s own dietary patterns ^(11,12), and have potential to inform the development of complex interventions for mother and child.

Here we describe a study in which we have determined empirical dietary patterns of 6-year-old children from the Children of SCOPE (CoS) study. Their mothers were participants in the Auckland New Zealand centre of the International Screening for Pregnancy Endpoints (SCOPE) study ⁽¹³⁾. The overall objective of CoS was to identify the early life modifiable determinants of obesity and insulin resistance in children at 5 to 6 years of age. We have examined relationships between dietary patterns and measures of childhood obesity as assessed by childhood body composition (waist, hip, and arm circumferences, body mass index (BMI), bioelectrical impedance analysis (BIA) derived body fat percentage, and sum of skinfold thicknesses (SST)) with the aim of informing new targets for intervention. Associations of maternal influences on childhood dietary patterns were also examined and taken into account as potential confounders for associations between childhood diet and adiposity.

Methods

Children of SCOPE

Children of SCOPE is a follow-up study of the offspring of women who participated in the Auckland arm of the Screening for Pregnancy Endpoints cohort study. SCOPE is a prospective international cohort study which aimed to identify factors in early pregnancy that predicted women at high risk of developing pre-eclampsia, spontaneous preterm birth and fetal growth restriction ⁽¹³⁾. Nulliparous women with a singleton pregnancy were recruited at 15 weeks’ gestation and a second research visit carried out at 20 weeks’ gestation; detailed demographic and clinical data were recorded at each visit and at delivery, and for any pregnancy complications. Details of the SCOPE study have been previously described ⁽¹³⁾. For a child to be eligible to participate in CoS, the mother agreed to be contacted about the follow-up study, and the child was aged between 5 and 6.25 years. Children with major health problems that might affect growth were excluded.

From the 2,032 mothers included in the Auckland SCOPE cohort, 1,208 (59%) mothers and their children were recruited. We previously reported no difference in maternal BMI between participants in CoS and the original complete Auckland cohort, however, the mothers in CoS were more likely to be older, White, have a higher educational attainment and higher socio-economic status, were less likely to smoke in their first trimester and were less likely to have delivered before 37 weeks ⁽¹⁴⁾. Of the 1,208 mother-child dyads, 35 were excluded due to missing or implausible percentage body fat. The final study population included 1,173 mother-child dyads and a total of 1142 children had information on dietary intake collected to extract dietary patterns. All women provided consent for participation of their children. Ethical approval was granted by the Auckland Ethics Committee (AKX/02/00/364).

Dietary assessment and dietary patterns

At the follow-up visit, a research nurse asked the child’s main caregiver to complete a Food Frequency Questionnaire (FFQ) about the child’s diet. This was based on an FFQ developed for the New Zealand Children's National Nutrition Survey ⁽¹⁵⁾ and requests information on the consumption of 118 food and beverage items. Accompanying this list was a multiple response grid which estimated frequency of consumption of the food and beverages in the preceding month, ranging from ‘never or less than once per month to ‘2 or more times per day’. Each food and beverage item had a standard serving size (e.g., slice of bread, apple). Where individual food items were unanswered, we imputed data using the MICE methodology. We used 10 burn-ins, followed by the construction of 10 imputed datasets. Data was imputed if less than 20% of items were missing.

Factor analysis with varimax rotation was performed to derive dietary patterns in each of the imputed datasets. The number of factors was chosen using the scree plot of eigenvalues and the interpretability of the factor loadings. Foods with a factor loading of ≥ ± 0.30 were considered to have a strong association with that factor. We then averaged factor loading for the dietary patterns across the imputed dataset. These factor loadings were then applied across an averaged imputed dataset constructed by averaging the responses across the imputed datasets. Sensitivity analyses were carried out to assess the MICE methodology to simple imputation of making an assumption that missed items were not answered because they were not consumed.

Childhood body composition

At the follow up visit, the child’s body composition was assessed using anthropometric and BIA measurements. Arm circumference (cm) was measured at the mid-point between the edge of the shoulder bone and bony tip of the right elbow. Waist circumference (cm) was assessed at the mid-point between the lowest rib and top of the hip. Hip circumference (cm) was taken as the distance around the maximal diameter of buttocks. For estimation of BMI (kg/m²), weight (kg) was measured in light clothing without shoes to the nearest 0.1kg and height (cm) assessed in a standing position with a stadiometer. Childhood obesity was defined as BMI z-score>2SD according to WHO’s child growth standards ⁽¹⁶⁾. Body fat percentage was determined by BIA (ImpediMed SFB7 tetra polar bioimpedance spectroscopy). Fat free mass (FFM) was calculated using: FFM = 0.65 (height²/impedance) + 0.686 * age + 0.15 ⁽¹⁷⁾. Body fat percentage was estimated using: (weight-FFM)/weight*100. Subscapular, triceps, biceps, and suprailiac skinfold thicknesses (mm) were measured using Holtain skinfold callipers and summed to give sum of skinfold thicknesses. All measurements were taken in triplicate and mean values calculated.

Covariates

In the SCOPE study, maternal demographic, clinical and lifestyle data were recorded. This included maternal age, BMI (kg/m²) at 15 weeks’ gestation, ethnicity, socioeconomic status [New Zealand Socio-economic Index 1996, education (years)], alcohol use pre-pregnancy, smoking during the 1st trimester, multivitamin use pre-pregnancy, maternal mood (Edinburgh Postnatal Depression Scale at 15 week visit), television (number of hours watched per day in the last month, evaluated at the 15 week visit), gestational hypertension (defined as systolic BP≥140 mmHg or diastolic BP≥90 mmHg on at least 2 occasions after the 20 week visit and before onset of labour), gestational diabetes mellitus diagnosis (any of :1) 75g OGTT fasting glucose ≥5.5 mmol/L and/or 2 hour ≥9.0 mmol/L, or 2) Polycose 1h glucose >11.0 mmol/L and no GTT available, or 3) Fasting Glucose ≥5.5 mmol/L and no polycose or GTT available, or 4) Random Glucose >11 mmol/L and no polycose, GTT or fasting glucose), biomarkers measured from maternal blood samples including placental growth factor (PlGF). At the time of birth, data were collected on premature birth, birthweight (grams), gestational age (weeks), child's age and sex and breastfeeding (exclusive breastfeeding at discharge form obstetric unit).

Statistical analysis

Descriptive data are presented as mean (standard deviation), frequency (percentage) or median (interquartile range) as appropriate. Multiple regression analysis was carried out to examine the associations between maternal influences (including maternal BMI, ethnicity, cigarette smoking in the first trimester, multivitamin use pre-pregnancy, breastfeeding, socio-economic status, education and age ^(12,18) and children’s dietary patterns. To investigate the relationship between childhood dietary patterns and body composition, linear regression was used. Two models were generated; model 1 where simple linear regression with no adjustment for confounding variables was performed and model 2, multiple linear regression including adjustment for confounders. Confounders were chosen a priori based on known associations with obesity ^{(14,19,28,29,20–27)}. The maternal and early post-natal exposures used in the adjusted model were maternal BMI, ethnicity, socioeconomic status, multivitamin use pre-pregnancy, alcohol intake pre-pregnancy, cigarette smoking in first trimester, time spent watching television in first trimester, depression in first trimester, gestational hypertension, GDM diagnosis, maternal plasma PlGF at 15 weeks’ gestation, breastfeeding, and infant sex. Standardised β-coefficients are presented which represent the SD in body composition measure per SD change in dietary pattern score, enabling comparison across outcomes. Data were analysed using Stata software, version 15.0 (StataCorp, College Station, Texas).

Results

Characteristics of the study population

The characteristics of the women and children are presented in Table 1. At 15 weeks’ gestation, the average age of the mothers was 31.0 (SD 4.4) years and the median BMI was 23.9 (21.8-26.6) kg/m². The majority of women were of White ethnicity (87.6%). The average age of the children at the follow up visit was 6.0 (0.2) years. A total of 176 (15.1%) children were overweight or obese according to WHO standards.

Table 1. Maternal and child characteristics in SCOPE and the Children of SCOPE studies (n=1142).

Maternal characteristics (15 weeks' gestation)	Mean (SD), median (IQR) or number (%)
Age (years)	31.0 (4.4)
BMI (kg/m2)	23.9 (21.8-26.6)
BMI (categorical)
< 18.5	8 (0.7%)
18.5 to 24.9	698 (61.2%)
25.0 to 29.9	321 (28.1%)
≥30	113 (9.9%)
Ethnicity
White	1001 (87.6%)
Asian	44 (3.9%)
Indian	36 (3.2%)
Māori	34 (2.9%)
Other	16 (1.4%)
Pacific	11 (0.9%)
Socio-economic status (NZSEI)
High (56-90)	315 (27.6%)
Medium (32-55)	677 (59.3%)
Low (10-31)	150 (13.1%)
Education (years)
<12	65 (5.7%)
12-13	1058 (92.6%)
>13	19 (1.6%)
Smoking in first trimester	107 (9.4%)
Alcohol intake pre-pregnancy	631 (54.5%)
Multivitamin use pre-pregnancy	377 (33.3%)
Gestational diabetes	18 (1.7%)
Gestational hypertension	61 (5.3%)
TV (hours watched per day)
<2	483 (42.4%)
2 - 4	586 (51.5%)
>4	70 (6.2%)
Characteristics at follow up
Maternal BMI (categorical)
< 18.5	23 (2.0%)
18.5 to 24.9	622 (54.5%)
25.0 to 29.9	327 (28.7%)
≥30	169 (14.8%)
Child's age	6.0 (0.2)
Child's sex (female)	578 (50.6%)
Child’s height (cm)	117.9 (4.9)
Child’s weight (kg)	21.7 (20.0-23.7)
Child's BMI (z-score)	0.28 (0.91)
Child overweight or obese (BMI z-score ≥1SD) *	176 (15.4)
Child overweight (BMI z-score ≥1SD, <2SD)	145 (12.7)
Child obese (BMI z-score ≥2SD)	31 (2.71)
Child’s percentage body fat	22.8 (6.3)
Child’s arm circumference (cm)	18.6 (1.7)
Child’s waist circumference (cm)	55.4 (4.0)
Child’s hip circumference (cm)	61.6 (4.9)
Child’s sum of skinfolds (mm)	30.5 (9.3)

Abbreviations: SES - socio-economic status, NZSEI - New Zealand Socio-Economic Index, BMI - body mass index, *World Health Organisation (2007)

Dietary patterns

1142 children had complete data in the imputed datasets. Factor analysis identified three distinct dietary patterns at age 6 years; for ease of description we have termed these as healthy, traditional and junk in line with other previously published studies ⁽³⁰⁾. The factor loadings for the dietary patterns are shown in Table S1. The ‘Healthy’ diet was characterised by high loadings including a number of fruits (berries, stone fruits, feijoas/kiwifruit, pineapple) and vegetables (avocado, tomato, green leafy vegetables, celery, cucumber, green beans, pepper). The ‘Traditional’ diet was based on a number of meat (meat and poultry in casseroles, roasted as part of a dish) and carbohydrate (potato, taro, kumara, and pumpkin either steamed, boiled or roasted). The ‘Junk’ diet was characterised by high loadings on candy bars, potato crisps, lollies or sweets, sausage rolls, and bought cake or muffin (Table 2). A number of other items in the Junk dietary pattern had loadings between 0.25 and 0.30 but were not consistently above 0.3 across factor analyses of the imputed datasets.

Table 2. Dietary patterns identified using factor analysis.

Variable	Healthy	Traditional	Junk
Avocado	0.45	0.03	-0.09
Green beans	0.34	0.15	-0.07
Berries	0.49	-0.13	-0.02
Cake/muffin/bun	0.15	0.05	0.32
Brown rice	0.36	0.03	-0.09
Candy bars	0.05	0.10	0.46
Celery	0.38	0.11	0.01
Cucumber	0.34	-0.06	-0.03
Dried fruit	0.32	0.06	-0.02
Feijoas/kiwifruit	0.38	0.12	0.02
Fish fillets not in breadcrumb/batter	0.31	0.11	-0.03
Green leafy vegetables	0.41	0.25	-0.08
Kumera/pumpkin roasted	0.18	0.42	0.01
Kumera/pumpkin steamed or boiled	0.19	0.37	-0.03
Lentils/chickpeas/other beans	0.37	0.09	-0.12
Lollies/sweets	-0.03	-0.09	0.31
Meat as part of dish (beef/pork/lamb)	0.05	0.36	0.02
Meat casseroles (beef,pork,lamb)	0.08	0.45	0.04
Meat roasted (beef/pork/lamb)	0.02	0.42	0.12
Mixed vegetables	0.18	0.40	0.09
Nuts	0.33	-0.01	-0.02
Other fruit	0.33	0.11	-0.02
Pepper	0.34	0.05	-0.05
Pineapple	0.37	0.09	0.08
Potato/other crisps	-0.17	0.06	0.34
Potato/taro roasted	0.09	0.34	0.08
Potato/taro steamed or boiled	0.03	0.36	0.01
Poultry as part of dish	0.11	0.38	0.02
Poultry casseroles	0.15	0.44	0.07
Poultry roasted	0.01	0.32	0.11
Sausage rolls	0.07	0.19	0.30
Stone fruit	0.45	-0.06	0.01
Tomato	0.43	0.00	-0.03
Yoghurt	0.31	0.02	-0.01

Factor loadings ≥± 0.3 for each identified dietary pattern

In sensitivity analyses, a factor analysis using data imputed by replacing missing data with never, resulted in very similar dietary patterns, and loadings. Examination of the imputed dataset showed that in general a large proportion of missing values on individual items were imputed as never.

Associations between maternal influences and dietary patterns

The dietary patterns had differing associations with maternal influences, which were robust to adjustment (Table 3). Children who had higher ‘Healthy’ dietary pattern scores were more likely to have mothers who had a lower early pregnancy BMI, of Indian ethnicity (and less likely to be Maori), who had a higher level of educational attainment and who took multivitamins pre-pregnancy (all P<0.05). Children who had higher ‘Traditional’ dietary pattern scores were more likely to have mothers of Pacific Islander ethnicity, who had a lower socioeconomic status were younger (all P<0.05). Children who had higher ‘Junk’ dietary pattern scores were more likely to have mothers of Indian or Pacific Islander ethnicity, that smoked in the first trimester and had a lower educational attainment (all P<0.05).

Table 3. Multiple regression analysis of the maternal influences associated with child’s dietary patterns at 6 years (n=1142).

Characteristics	Adjusted coefficient (β) (95% CI)	P	Adjusted coefficient (β) (95% CI)	P	Adjusted coefficient (β) (95% CI)	P
	Healthy		Traditional		Junk
Early-pregnancy BMI (kg/m2)	-0.03 (-0.04 to -0.02)	<0.001	0.01 (-0.001 to 0.02)	0.071	0.01 (-0.003 to 0.02)
						0.140
Ethnicity
White	Reference	0.002	Reference	<0.001	Reference	0.002
Asian	-0.10 (-0.36 to 0.15)		0.14 (-0.10 to 0.37)		0.002 (-0.23 to 0.23)
Indian	0.32 (0.04 to 0.61)		-0.05 (-0.31 to 0.21)		0.28 (0.03 to 0.54)
Maori	-0.32 (-0.61 to -0.02)		0.22 (-0.05 to 0.48)		0.12 (-0.14 to 0.38)
Other	0.64 (0.21 to 1.07)		0.60 (0.20 to 0.99)		0.47 (0.08 to 0.85)
Pacific Islander	-0.21 (-0.73 to 0.30)		0.79 (0.32 to 1.26)		0.70 (0.24 to 1.16)
Cigarette smoking in first trimester	0.10 (-0.08 to 0.27)	0.274	0.01 (-0.15 to 0.17)	0.942	0.25 (0.09 to 0.41)	0.002
Multivitamin use pre-pregnancy	0.13 (0.02 to 0.24)	0.016	-0.09 (-0.19 to 0.01)	0.064	-0.01 (-0.10 to 0.08)	0.833
Breastfeeding	0.10 (-0.02 to 0.22)	0.098	-0.02 (-0.13 to 0.08)	0.688	0.01 (-0.10 to 0.11)	0.920
Socio-economic status
High	-0.02 (-0.13 to 0.10)	0.311	-0.21 (-0.31 to -0.10)	<0.001	0.02 (-0.08 to 0.12)	0.290
Medium	Reference		Reference		Reference
Low	-0.12 (-0.28 to 0.04)		0.09 (-0.05 to 0.24)		0.11 (-0.03 to 0.26)
Education (years)	0.12 (0.04 to 0.20)	0.002	-0.04 (-0.11 to 0.03)	0.225	-0.09 (-0.16 to -0.02)	0.010
Age (years)	0.01 (-0.01 to 0.02)	0.363	-0.02 (-0.03 to -0.01)	<0.001	0.004 (-0.01 to 0.01)	0.496

All of the maternal characteristics have been adjusted for each other in the multivariate analysis.

Associations between children’s dietary patterns and body composition

The association between children’s dietary patterns and body composition at aged 6 years is shown in Table 4. Children who had higher ‘Healthy’ pattern scores were less likely to be obese according to WHO child growth standards in Models 1 and 2 (adjusted OR=0.62; 95% CI 0.39, 1.00).

Table 4. Multiple regression analysis of the dietary patterns associated with body composition at age 6.

	Model 1 (unadjusted)			Model 2 (adjusted)
Body composition measure	n	Standardised coefficient (β)/odds ratioc (95% CI)	P	n	Standardised coefficient (β)/ odds ratioc (95% CI)	P
Healthy pattern
Percentage body fat	1142	-0.04 (-0.09 to 0.01)	0.101	1025	-0.02 (-0.07 to 0.04)	0.482
BMI z-scorea	1142	-0.05 (-0.11 to 0.002)	0.060	1025	-0.01 (-0.07 to 0.05)	0.677
Arm (cm)	1142	-0.02 (-0.07 to 0.03)	0.417	1025	0.01 (-0.04 to 0.06)	0.728
Waist (cm)	1142	0.001 (-0.05 to 0.05)	0.966	1025	0.02 (-0.03 to 0.08)	0.362
Hip (cm)	1142	-0.02 (-0.07 to 0.03)	0.370	1025	0.01 (-0.04 to 0.06)	0.711
Sum of skinfolds (mm) b	1133	-0.04 (-0.09 to 0.01)	0.120	1019	-0.03 (-0.08 to 0.03)	0.327
Overweight or obese (BMI z-score >1SD) c	1142	0.76 (0.63 to 0.92)	0.005	1025	0.85 (0.69 to 1.05)	0.125
Obese (BMI z-score >2SD) c	1142	0.52c (0.33 to 0.80)	0.003	971	0.62c (0.39 to 1.00)	0.050
Traditional pattern
Percentage body fat	1142	0.06 (0.02 to 0.11)	0.006	1025	0.04 (-0.01 to 0.09)	0.097
BMI z-scorea	1142	0.08 (0.03 to 0.13)	0.003	1025	0.04 (-0.02 to 0.09)	0.176
Arm (cm)	1142	0.05 (0.002 to 0.09)	0.042	1025	0.02 (-0.03 to 0.07)	0.479
Waist (cm)	1142	0.07 (0.02 to 0.12)	0.003	1025	0.05 (-0.002 to 0.09)	0.060
Hip (cm)	1142	0.06 (0.01 to 0.11)	0.011	1025	0.02 (-0.03 to 0.07)	0.396
Sum of skinfolds (mm) b	1133	0.03 (-0.02 to 0.08)	0.220	1019	0.01 (-0.04 to 0.06)	0.679
Overweight or obese (BMI z-score >1SD) c	1142	1.34 (1.09 to 1.64)	0.006	1025	1.19 (0.95 to 1.50)	0.135
Obese (BMI z-score >2SD) c	1142	0.93c (0.59 to 1.45)	0.740	971	0.89c (0.53 t0 1.49)	0.660
Junk pattern
Percentage body fat	1142	0.04 (0.0002 to 0.09)	0.049	1025	0.02 (-0.03 to 0.07)	0.486
BMI z-scorea	1142	0.05 (0.004 to 0.10)	0.034	1025	0.02 (-0.03 to 0.08)	0.372
Arm (cm)	1142	0.10 (0.06 to 0.15)	<0.001	1025	0.08 (0.04 to 0.13)	<0.001
Waist (cm)	1142	0.05 (0.01 to 0.10)	0.016	1025	0.03 (-0.01 to 0.08)	0.158
Hip (cm)	1142	0.07 (0.03 to 0.11)	0.002	1025	0.05 (0.01 to 0.10)	0.028
Sum of skinfolds (mm) b	1133	0.09 (0.05 to 0.13)	<0.001	1019	0.07 (0.03 to 0.12)	0.002
Overweight or obese (BMI z-score >1SD) c	1142	1.36 (1.11 to 1.67)	0.003	1025	1.24 (0.99 to 1.57)	0.066
Obese (BMI z-score >2SD) c	1142	1.82c (1.19 to 2.80)	0.006	971	1.74c (1.07 to 2.82)	0.025

Standardised coefficient (β), representing the standard deviation (SD) change and 95% confidence interval in body composition measure per SD change in dietary pattern score enabling comparison across outcomes; Model 1 (simple regression) - association between dietary patterns and childhood body composition at age 6 years, Model 2 (multiple regression) – association between dietary patterns and childhood body composition, adjusting for infant mode of feeding, infant sex, and maternal ethnicity, BMI, socioeconomic status, multivitamin use pre-pregnancy, alcohol intake pre-pregnancy, cigarette smoking in first trimester, time spent watching television in first trimester, depression in first trimester, gestational hypertension, GDM diagnosis, and plasma placental growth factor.

^aWorld Health Organisation (2007) BMI for age z-score;

^bsum of triceps, biceps, subscapular and suprailiac skinfold thicknesses;

^codds ratio

Children who had higher ‘Junk’ pattern scores had greater arm (adjusted (a)β=0.08; 95%CI 0.04, 0.13) and hip (aβ=0.05; 95%CI 0.01, 0.10) circumferences, and SST (aβ=0.07; 95%CI 0.03, 0.12). Furthermore, children who had higher ‘Junk’ pattern scores were more likely to be obese (OR=1.74; 95%CI 1.07, 2.82). These associations were robust to adjustments for confounders.

Children who had higher ‘Traditional’ dietary pattern scores had greater arm, waist and hip circumferences, body fat percentage and BMI in the univariable analysis. These associations were not robust to adjustments for confounders.

Discussion

In this large contemporary mother and child cohort, we found one distinct empirical childhood dietary pattern to be associated with greater adiposity and obesity risk in children aged 6 years. This pattern was characterised by high intakes of foods and beverages high in sugar and/or fat including saturated fat. A healthy dietary pattern characterised by a number of fruits and vegetables was associated with a reduced risk of obesity in a small number of children. Furthermore, we were able to characterise influences of maternal antenatal exposures on childhood dietary patterns which included maternal ethnicity, age, BMI, education, socioeconomic status and smoking in pregnancy.

The majority of reports of childhood dietary patterns have addressed the hypothesis that dietary behaviours in early childhood can influence the risk of obesity in later childhood. Several studies have shown a strong relationship between unhealthy dietary patterns characterised by energy dense, low fibre and high fat foods and their later risk of obesity ^(31,32). Few studies have examined the relationship between empirical dietary patterns and childhood body composition measured simultaneously. In one of the few to have specifically addressed this association, the Lifeways Cross-Generational Cohort study in Irish children aged 5 years showed that the upper tertiles of a "cereal and juice" dietary pattern and the middle tertile of a ‘pasta and veg’ dietary pattern were negatively associated with overweight and obesity ⁽³³⁾. Our findings, together with observations that diet in early childhood is a risk for later obesity may reflect unchanging dietary patterns throughout early and mid-childhood ⁽⁴⁾, suggesting that interventions which specifically target foods and snacks high in sugar and fat in early childhood could play a role in reducing later risk of obesity. The observation that the relationship between the Junk and Healthy patterns and BMI observed in this study was only evident in children classified as obese is particularly relevant to public health measures for the prevention of childhood obesity.

The three dietary patterns identified in this study (‘Traditional’, ‘Junk’ and ‘Healthy’) are consistent with those described in the New Zealand birth cohort study which assessed dietary patterns in Auckland children aged 3.5 and 7 years ⁽³⁰⁾. Similarities were observed for high factor loadings for the ‘traditional’ diet of meat, vegetables and potatoes, the ‘healthy’ diet of fruit, vegetables and nuts, and the junk diet of confectionary.

We identified important associations between maternal influences and childhood dietary patterns. Better quality diets were more common in children who had mothers with higher educational attainment, and who had a lower BMI, similar to observations in the Southampton Women’s Survey ^(12,34). Consistent with ALSPAC data, poorer diets were more common in children who were born to mothers who had lower educational attainment ⁽¹⁸⁾. Furthermore, the mothers of children with poorer diets were more likely to smoke in pregnancy, which is similar to the New Zealand birth cohort study ⁽³⁰⁾. Recently, we reported on the relationship between maternal BMI and childhood obesity risk in the Children of SCOPE cohort. Maternal BMI was independently associated with childhood BMI z-score, sum of skinfolds and percentage body fat (all P<0.001) ⁽¹⁴⁾. The global increase in obesity, which is reflected in women who present for antenatal care has led to the concern that obesity in pregnancy may contribute to childhood obesity risk, with evidence from observational studies to support this link ⁽³⁵⁾. Exposure in utero to the metabolic consequences of a high maternal BMI including dyslipidaemia, inflammatory markers, hyperinsulinaemia and hyperleptinaemia appears to influence offspring obesity risk ^(36,37). This highlights the need for public health interventions to promote a healthy BMI and better quality diet in reproductive aged women.

Strengths of the current study include this being an addition to a very sparse literature which has defined the dietary patterns associated with childhood obesity. Whilst the assumption is often made that foods and snacks high in sugar and fat are major contributors, there is limited evidence to support this widely held assumption in paediatric cohorts. Furthermore, our observations have been made in a large cohort, compared to previous studies of childhood obesity in mother child cohorts. By focusing on obesity in the children, the Children of SCOPE protocol prospectively included detailed measurements of childhood adiposity and a comprehensive dataset to examine maternal influences on childhood diet. By characterising dietary patterns, we have been able to capture the complexity of the diet while overcoming the limitations of single nutrient assessment ⁽⁶⁾.

Whilst this study provides evidence that specific dietary patterns in children are linked to childhood obesity, there are some limitations. At follow-up, data from 1,208 (59%) of the 2,032 mothers and their children from the original Auckland arm of the SCOPE cohort were available, which could lead to erroneous conclusions if the relationships between dietary patterns and childhood body composition differ between those included in follow up and those not. Being observational in design, we are only able to examine associations and not infer causation between dietary patterns and childhood obesity development. We assessed dietary intake using an FFQ, which may be prone to bias ⁽³⁸⁾. Factor analysis was used to derive empirical dietary patterns which involves several subjective decisions including the number of factors to extract, rotation method and naming of the factors ⁽³⁹⁾. We adjusted for several potentially confounding variables in the assessment of the relationship between maternal influences and childhood dietary patterns and between childhood dietary patterns and obesity. However, the possibility of residual confounding due to unmeasured lifestyle factors cannot be excluded. The relationship between physical activity and inactivity was not assessed in the present study. WHO guidelines on childhood obesity prevention include promoting physical activity among children in the first few years of life in addition to focusing on dietary intake ⁽⁴⁰⁾. Evidence from cross-sectional studies examining the association between physical activity in children and adiposity suggest that higher levels of physical activity are related to reduced adiposity ⁽⁴¹⁾. Associations with sedentary time are less consistent ⁽⁴²⁾. Future studies should include physical activity in assessing the relationship between childhood health behaviours and obesity risk. Finally, the majority of women in this study were of European descent and were well educated and were not a representative cohort of New Zealand women and children.

Conclusions

This mother-child cohort study with multiple measures of childhood body composition provides support for a relationship between empirical dietary patterns representing overall diet and childhood adiposity. A dietary pattern characterised by consumption of high sugar and fat including saturated fat foods was associated with greater body circumferences, sum of skinfolds, and obesity risk in children aged 6 years, while a “Healthy” dietary pattern offered some apparent protection against obesity. We also identified a number of maternal exposures that were associated with eating patterns in early childhood providing insights into important maternal influences on children’s dietary intake. Our data adds to the growing evidence that targeting maternal obesity and unhealthy dietary patterns associated with early childhood adiposity could inform public health strategies to reduce the prevalence of childhood obesity.