Nutrient intake and adequacy in children with autism spectrum disorder: EPINED epidemiological study

Abstract

This article assesses nutritional intake and adequacy in children with autism spectrum disorder (ASD), subdiagnostic autistic symptoms and children with typical development (TD). In total, 77 children diagnosed with ASD, 40 with subdiagnostic autistic symptoms and 333 children with TD were assessed. A validated food frequency questionnaire was used. Very few nutritional differences were found between ASD and TD groups. Preschool children with ASD and subdiagnostic autistic symptoms had slightly lower intake of monounsaturated fatty acids (MUFA), vitamin D and vitamin B12, and primary school children with ASD and subdiagnostic autistic symptoms had slightly higher intake of protein, cholesterol, thiamine and niacin, and a higher percentage of obesity than children with TD. All samples had an unbalanced energy intake (high in added sugars, fats and saturated fatty acids (SFAs); extremely inadequate intake (80%–100%) of vitamins D and E; high intake (50%–80%) of fibre, b-carotene (except preschool children with TD), calcium (except preschool children) and magnesium). The fact that differences between diagnoses are scarce may be related to the low level of ASD severity in this school sample.

Lay abstract

Children with autism spectrum disorder (ASD) have a fivefold elevated risk of developing eating problems, which predisposes them to nutritional deficiencies. This study assesses nutritional intake and adequacy in children with ASD, subdiagnostic autistic symptoms and typically developing (TD) children. Preschool children with ASD and subdiagnostic symptoms had slightly lower intake of monounsaturated fatty acids (MUFA), vitamin D and vitamin B12. Primary school children with ASD and subdiagnostic symptoms had slightly higher intake of protein, cholesterol, thiamine and niacin, and a higher percentage of obesity than children with TD. All children had a high intake in sugars, fats and saturated fatty acids; a very highly inadequate intake of vitamins (vitamins D and E), fibre, b-carotene, calcium and magnesium; and a moderately inadequate intake of vitamin C, folate and iron. However, although all children need nutrition advice, children with ASD and subdiagnostic autistic symptoms had a poorer quality diet than those with TD.

Keywords

autism spectrum disorder mineral nutrition/feeding preschool children primary school children vitamin

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition of neurobiological origin that manifests in childhood. It involves difficulties in the development of social communication and interaction and restricted or repetitive patterns of behaviour, interests or activities (American Psychiatric Association, 2013). In the past few years, research has recognised ASD as a continuum of severity ranging from subclinical autistic traits or symptoms to full syndrome (Constantino & Charman, 2016; Morales-Hidalgo et al., 2017). The prevalence has increased in recent years, with ASD being diagnosed in 1 of 53/54 children (Maenner et al., 2020; Morales-Hidalgo et al., 2021) and subdiagnostic autistic symptoms requiring support in 3%–4% of the child population (Jussila et al., 2020; Morales-Hidalgo et al., 2018).

Certain characteristics of ASD, such as behavioural rigidity, a need for sameness, routines, and repetitive behaviours, can affect food consumption (Matheson & Douglas, 2017). Children with ASD also present a different sensory profile with hypo- or hyperreactivity to tactile, olfactory and visual stimulus (Nadon et al., 2011a) that can cause strict food selectivity conditioned by texture, appearance, taste or temperature (Hubbard et al., 2014; Nadon et al., 2011b). This leads children to reject certain foods and accept only a small proportion of selected foods that they consume repeatedly. In some cases, children also display motor problems associated with chewing and swallowing food (Nadon et al., 2011b), biological food intolerance or gastrointestinal problems (Barnhill et al., 2018; Leader et al., 2020). These associated conditions limit the consumption of certain foods: acidic foods and roughage, for example, are often rejected, whereas sweet foods with a high energy density are welcomed (Hubbard et al., 2014; Leader et al., 2020).

Several studies of food consumption and nutritional intake in children with ASD have reported little variety in the diets of these children, a general preference for energy-dense foods, and a dislike for fruits, vegetables and dairy products (Canals-Sans et al., 2022; Esteban-Figuerola et al., 2019). Although dietary energy intakes are similar to controls, the food selectivity shown by many children with ASD seems to be related to deficiency of some micronutrients (Tomova et al., 2020). These authors also found an association between altered intakes of some nutrients and the specificity of the gut microbiota in children with ASD. In this sense, gut microbiota dysregulation may play an important role in the pathogenesis of inflammation or synthesis of some neurotransmitters and thus contribute to the manifestation of ASD symptoms (Iglesias-Vázquez et al., 2020; Kong et al., 2019). All these mentioned relationships could open up a new field of therapeutic action for ASD through diet. Food intervention in children with ASD could help to modify the intestinal microbiota in such a way as to improve clinical, gastrointestinal and immune status (Ristori et al., 2019; Tomova et al., 2020). Other authors have suggested that children with ASD have a greater need for certain antioxidant vitamins than children with typical development (TD) since oxidative stress biomarkers and other metabolic abnormalities were found to be higher in children with ASD (Bjørklund et al., 2019; Geraghty et al., 2010; Leader et al., 2020).

The consumption pattern of children with ASD is far from the balanced and varied consumption pattern that is recommended for all children. This can lead to alterations in nutritional intake that affect health and growth. Recent studies (Esteban-Figuerola et al., 2019; Sharp et al., 2013; Tomova et al., 2020) have reported a higher degree of nutritional inadequacy in children with ASD than in children with TD in relation to certain nutrients (docosahexaenoic and docosapentanoic acid, calcium, iron, phosphorus, selenium, zinc, vitamins A, D, C, B6, B12 and thiamine, riboflavin, niacin and/or folate), although no differences in macronutrients have been found (Tomova et al., 2020). However, the results in the literature are controversial. Some studies have observed an adequate intake of protein, phosphorus, selenium, thiamine, riboflavin and vitamin B12 in children with ASD (Esteban-Figuerola et al., 2019; Hyman et al., 2012), while others have found an inadequate intake of these nutrients (Andrew & Sullivan, 2010; Hyman et al., 2012; Vogelaar, 2000; Xia et al., 2010) or deficiencies in nutrients such as vitamin A and fibre (Attlee et al., 2015; Guo et al., 2020; Zimmer et al., 2012).

We believe it is important to know the nutritional intake of children with ASD from the earliest age and in pre-adolescence so that the degree of nutritional inadequacy these children may present can be identified. Several studies have described the energy and nutrient intake of children with ASD from clinical samples, but few have done so in inclusive education school population. We believe it is important (and novel) to describe the nutritional intake of children not only in severe cases of ASD but also in milder/moderate profiles and subdiagnostic autistic symptoms, which have a growing increasing interest in the literature (Dell’Osso et al., 2016, 2018). This knowledge should enable us to propose primary prevention strategies for a condition that is highly prevalent today. Thus, the aim of this study is to assess nutritional intake and adequacy in preschool and primary school children with ASD, subdiagnostic autistic symptoms and TD. We hypothesise that: (1) children with ASD will have a lower intake of certain nutrients than children with TD; and (2) children with ASD will have a lower than recommended intake of certain nutrients.

Methods

Study design, sample

This two-phase epidemiological study was conducted in the province of Tarragona, Spain, from 2014 to 2019. It was approved by the Research and Ethics Committee of Sant Joan University Hospital in Reus, Spain (13-10-31/10proj5).

Children from two age groups participated in the study: preschool children (3–6 years old) and primary school children at the end of the primary education stage (10–12 years old). Of the 296 inclusive schools in the province, 86 were randomly selected for a total of 6894 children (3374 children aged from 3 to 6 years and 3520 children aged from 10 to 12 years). Only children whose parents signed the informed consent form and whose parents and teachers completed screening tests to detect the likelihood of ASD and attention deficit hyperactivity disorder (ADHD) were included (n = 3713). The participation rate was 54%, which is consistent with the rates in other epidemiological studies, which in turn have shown a decrease in recent years and lower participation rates when families are involved (Galea & Tracy, 2007; Morton et al., 2006). In the second phase, children identified as having likelihood for ASD/ADHD and a control group of children without that likelihood (chosen at random from the same classrooms) were individually assessed to confirm the diagnoses (n = 760). Children with ADHD (n = 259) were excluded. Of the 501 children who remained, 61 were diagnosed with ASD, 43 presented autistic manifestations but did not meet every Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for ASD (classified as subdiagnostic autistic symptoms) and 397 were diagnosed with neither ASD nor ADHD (classified as controls or TD). To increase the sample of children with ASD, 18 children were selected from outpatient clinical services. The participants in the clinical sample came from the same region, had the same age and socioeconomic status, and went to an ordinary school (not a special school) like the other participants. Children with neurodevelopmental conditions other than ASD (e.g. ADHD or intellectual disability) and those whose parents did not complete the food questionnaires were excluded. Our final sample consisted of 77 children with ASD, 40 children with subdiagnostic autistic symptoms and 333 children with TD.

The socioeconomic status of all subjects was estimated using the Hollingshead Index (Hollingshead, 2011) and the sociodemographic data reported by their families.

Community involvement included special education teachers who helped to develop the new ASD screening test for the school population. The teachers of all participants provided feedback on the results and helped us to encourage the children’s families to get involved in the project.

Psychological assessment and diagnostic procedure

In the first phase, a screening process for ASD was carried out through parents and teachers, and sociodemographic characteristics of the population were collected. To screen for the likelihood of ASD, the parents completed the Childhood Autism Spectrum Test (CAST; Morales-Hidalgo, Roigé-Castellví, et al., 2017; Scott et al., 2002) and the teachers completed the EduTEA questionnaire (https://psico.fcep.urv.cat/Q4/EduTEA; Morales-Hidalgo, Hernández-Martínez, et al., 2017). The psychometric properties of these instruments in the EPINED (Neurodevelopmental Disorders Epidemiological Research Project) sample demonstrated a high internal reliability (CAST: α = 0.826; EduTEA: α = 0.97). The cut-off score of 10 in EduTEA gives high values of sensitivity (87%), specificity (91.2%) and positive predictive value (0.87). The cut-off score of 15 in CAST presented a high sensitivity (83.9%) and specificity (92.5%) and a positive predictive value (0.63).

In the second phase, the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Lord et al., 2012) and the Autism Diagnostic Interview–Revised (ADI-R; Rutter et al., 2003) were used to diagnose ASD (DSM-5 criteria). Autism severity was classified according to the ADOS-2 cut-offs suggested by Gotham et al. (2009), where a score of <3 was ‘non-ASD’, a score of 4–5 was ‘ASD’ and a score of 6–10 was ‘autism’. Subdiagnostic autistic symptoms were considered when the child scored slightly below the cut-off point scores in diagnostic algorithms with the ADI-R and ADOS-2 and two professionals agreed that he or she did not meet all the diagnostic criteria of the DSM-5. These children exhibited symptoms of both socio-communicative problems and repetitive behavioural patterns without meeting criteria of other diagnostic categories according to the Schedule for Affective Disorders and Schizophrenia for School Age Children Present and Lifetime (K-SADS-PL) interview (Kaufman et al., 1997), excluding also social communication disorder and stereotypic movement disorder. ASD, subdiagnostic autistic symptoms and TD groups showed significantly different autistic symptomatology severity profiles considering the mean scores of the screening and diagnostic instruments (see Table 1). Also, in the second phase, Intelligence Quotient (IQ) was assessed by Wechsler Scales for preschool (Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (WPPSI-IV); Wechsler, 2014) and school-age children (Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV); Wechsler, 2005). The diagnoses were conducted by two trained researchers (a psychologist and a psychiatrist who had experience with ASD).

Table 1.

Sociodemographic, anthropometrical and psychological characteristics by age group and subgroups of diagnosis.

	Preschool-age children				Primary-school-age children
	ASD^a (n = 36)	ASD Subdiagnostic^b (n = 21)	TD^c (n = 142)	p ^(a,b,c)	ASD^a (n = 41)	ASD-SUBCLINICAL^b (n = 19)	TD^c (n = 191)	p ^(a,b,c)
Age (years)^#	5.07 (0.67)	4.79 (0.40)	5.09 (0.51)	0.050 (0.008)^{b c}	11.19 (1.48)	10.96 (0.54)	11.06 (0.52)	0.499
Gender (males), % (n)	80.60 (29)	71.40 (15)	57.70 (82)	0.029 (0.012)^{a c}	85.40 (35)	84.20 (16)	56.00 (107)	<0.001 (<0.001)^{a c} (0.017)^{b c}
Hollingshead classification, % (n)
Low	22.20 (8)	23.80 (5)	12.00 (17)	0.153	9.80 (4)	36.80 (7)	15.20 (29)	0.024 (0.012)^{a b} (0.017)^{b c}
Medium-high	77.80 (28)	76.20 (16)	88.00 (125)	0.153	90.20 (37)	63.20 (12)	84.80 (162)	0.024 (0.012)^{a b} (0.017)^{b c}
Diet Quality Index (score)^#	57.07 (10.80)	61.05 (9.63)	61.66 (7.20)	0.012 (0.020)^{a c}	56.84 (8.93)	64.50 (8.24)	60.08 (6.31)	<0.001 (0.032)^{a c} (0.002)^{a b} (0.034)^{b c}
BMI-for-age categories (z-score), % (n)
Normoweight (Reference group)	72.20 (26)	66.70 (14)	71.10 (101)	0.474 0.240	36.60 (15)	57.90 (11)	54.50 (104)	0.249 0.046
Overweight	11.10 (4)	19.00 (4)	21.80 (31)		34.10 (14)	36.80 (7)	26.20 (50)
Obese	16.70 (6)	14.30 (3)	7.00 (10)		29.30 (12)	5.30 (1)	19.30 (37)
Severity of ASD symptoms (score)^#
EduTEA total score	20.41 (9.41)	12.63 (8.40)	4.81 (6.28)	<0.001(<0.001)^a c/b c (0.006)^{a b}	19.48 (9.31)	10.35 (7.99)	3.04 (4.78)	<0.001(<0.001)^a c/b c (0.004)^{a b}
CAST total score	17.81 (4.65)	13.92 (4.66)	8.03 (4.15)	<0.001(<0.001)^a c/b c (0.003)^{a b}	17.76 (6.13)	12.46 (4.86)	7.38 (4.29)	<0.001(<0.001)^a c/b c (0.001)^{a b}
ADOS-2 mean severity score	5.53 (1.91)	3.43 (1.91)	1.44 (1.00)	<0.001(<0.001)^a c/a b/b c	5.12 (2.10)	3.68 (1.74)	1.19 (0.60)	<0.001(<0.001)^a c/b c (.012)^{a b}
ADI-R social interaction impairments	13.66 (5.35)	8.00 (4.49)	1.95 (1.88)	<0.001(<0.001)^a c/a b/b c	14.24 (6.41)	6.60 (2.83)	1.85 (2.13)	<0.001(<0.001)^a c/a b/b c
ADI-R communication impairments	9.84 (3.16)	5.88 (3.86)	1.44 (1.51)	<0.001(<0.001)^a c/a b/b c	12.08 (4.22)	5.76 (3.10)	1.58 (1.67)	<0.001(<0.001)^a c/a b/b c
ADI-R repetitive patterns of behaviour^#	4.56 (2.08)	2.08 (1.14)	.80 (.96)	<0.001(<0.001)^a c/a b/b c	4.04 (2.62)	2.44 (1.21)	0.56 (0.88)	<0.001(<0.001)^a c/a b/b c
Intelligence quotient (score)^#	87.20 (18.88)	93.08 (17.98)	101.73 (12.43)	<0.001(<0.001) ^{a c}	100.35 (18.42)	89.82 (14.55)	104.84 (13.22)	<0.001(<0.001) ^{a c}

ASD: autism spectrum disorder; TD: children with typical development; BMI: body mass index; CAST: Childhood Autism Spectrum Test; ADOS-2: Autism Diagnostic Observation Schedule, Second Edition.

Mean (SD).

Autism Spectrum Disorder; ^bSubdiagnostic autistic symptoms; ^cChildren with typical development: p-value ^(a,b,c) between subgroups of diagnosis.

Anthropometric measures

Anthropometric measures were taken in accordance with the International Society for the Advancement of Kinanthropometry (ISAK) protocols. To measure height (cm), a SECA® stadiometer accurate to 0.1 mm (PERILB-STND) was used, while TANITA scales (BC 420SMA) were used to measure weight (kg). Body mass index (BMI) (kg/m²) was obtained and recorded as z-scores (de Onis et al., 2007; World Health Organization [WHO], 2007). The BMIs recorded as z-scores were divided into the following categories: normoweight, overweight and obese.

Energy and nutrient intake

The children’s food consumption frequency was completed by the participants’ parents using the food frequency questionnaire (FFQ). Validated in Spanish, this instrument comprises 41 items for young children (Esteban-Figuerola et al., 2019) and 45 items for adolescents (Trinidad et al., 2008). The participants were asked about their usual food consumption habits in terms of serving size and the times of day when they usually ate. The questionnaires were carried out during the academic year (from September to June).

First, the frequency of consumption was transformed into grams per day based on the portion size described for each item and the age group in our population (Agència de Salut Pública de Catalunya, 2005). Next, the energy and nutrients corresponding to each item were calculated using a composition table specifically adapted to this questionnaire. For this calculation, the percentage consumption of the foods included in each item was considered for each population (e.g. fruit: % apples, % pears, % peaches) based on food consumption assessment studies conducted by our research group (Aparicio et al., 2015; Arija et al., 1996; Jardí, Aranda, Bedmar, & Arija, 2019; Jardí, Aranda, Bedmar, Ribot, et al., 2019) for the same population. These proportions were applied to the nutritional composition of each item in the REGAL food composition table (Répertoire Géneral des Aliments; Favier et al., 1997). This was complemented by the Mataix Verdú food composition table for specific Spanish foods (Mataix et al., 2009). The total intake of natural sugars and added sugars was calculated following WHO recommendations (WHO, 2015). As regards simple carbohydrates, the natural sugars were whole fruits, vegetables, milk and cereals (rice, bread, pasta, flour), whereas the free sugar group included the sugar in sweetened drinks and (commercial and natural) juice, sweet cereals such as sweetened breakfast cereals, cookies, cakes and other sweet foods such as chocolate and dairy desserts, in accordance with WHO (2015) definitions.

In terms of energy, we calculated the percentage of children with intakes under 75% and over 125% of the Estimated Average Requirements (EAR) recommended by the European Food Safety Authority (EFSA, 2017). For nutrients, the probability of inadequate intake was determined as being below the EAR based on EFSA-recommended values (EFSA, 2017) using the following formula: Z = 1 – (CDF (AR – μ) / SD) × 100, where CDF assesses the cumulative distribution function, μ is the subject’s nutrient intake, and SD is 1 standard deviation of EAR (Average Requirement) (calculated as AR × 0.1) (Institute of Medicine (US) Subcommittee on Interpretation and Uses of Dietary Reference Intakes, & Institute of Medicine (US) Standing Committee on the Scientific Evaluation of Dietary Reference Intakes [IOM], 2000). When EAR was not determined, we used adequate intake, as in the case of magnesium, vitamin B12, vitamin D and vitamin E. The percentage of energy provided by macronutrients was calculated and compared with the values most frequently recommended by international organisations, that is, protein 10%–30%, carbohydrates >50%, fat <30% (IOM, 2000), saturated fatty acids (SFAs) <10%, polyunsaturated fatty acids (PUFAs) 7%–10%, monounsaturated fatty acids (MUFAs) >15% and added sugars <10% (WHO, 2015).

Diet quality was also estimated using the Spanish Quality Diet Index (SQDI) proposed by Norte-Navarro and Ortiz-Moncada (2011) based on the food recommendations of the Sociedad Española de Nutrición Comunitaria (2004). The SQDI includes nine food groups based on their nutritional quality. Thus, meat, fish and eggs were re-grouped into a single group, and sweets and sweet cereals also were grouped into a single group. The consumption of each group by serving was compared to the recommended consumption and scored 0–100. The final index scores were classified as follows: >80 points, ‘healthy’; 50–79 points, ‘needs to improve’; and <49 points, ‘unhealthy’.

Statistical analysis

The variables were described and compared by age group and diagnosis: children diagnosed with ASD, children with subdiagnostic autistic symptoms and TD. The results were expressed as mean and standard deviation for quantitative variables and as percentages for qualitative variables. Analysis of variance (ANOVA) and Chi-square analyses were used to compare variables, with the correction of Bonferroni for multiple comparisons. The results were adjusted by socioeconomic level. The significance level for all statistical comparisons was set at p < 0.05. The data were analysed using SPSS Statistics 25.0.

Results

Table 1 shows the sociodemographic, anthropometric and psychological characteristics of the children by age group and diagnosis (ASD, subdiagnostic autistic symptoms and TD). Children with ASD scored lower in the diet quality index. The mean ADOS-2 score for children with ASD was in the non-severe range. Primary school children with ASD had higher BMIs than those with TD. The mean IQ score was in the normal range for all groups. However, 12.9% of children with ASD and 20% of children with subdiagnostic autistic symptoms had IQ ⩽70 (ID). The TD group did not include children with ID.

Tables 2 and 3 describe the energy and nutrient intakes and the probabilities of inadequate intake below the EAR in each age group. We found that a high percentage of children had an energy intake of over 125% of the EAR (21.1%–47.9%) but no differences between children with ASD, subdiagnostic autistic symptoms and children with TD in either age group. We also found that a significantly higher percentage of preschoolers with ASD and subdiagnostic autistic symptoms than those with TD had inadequate intakes of b-carotene (62.40%, 64.00% and 37.70%, respectively), vitamin B6 (2.60%, 4.30% and 0.0%, respectively) and vitamin B12 (5.10%, 1.50% and 0.0%, respectively). Also, a higher percentage of children with subdiagnostic autistic symptoms had inadequate intakes of thiamine, riboflavin and niacin than children with TD. For MUFA, vitamin D and vitamin B12, only children with ASD had a lower mean intake than children with TD (35.80 g/day vs 38.00 g/day; 1.47 µg/day vs 2.11 µg/day; and 3.77 µg/day vs 4.62 µg/day), although only the intake of vitamin D was inadequate. The data on primary school children were different from those on preschool children (see Table 3). Primary school children with ASD had a higher mean intake of protein (73.1 g/day vs 65.5 g/day), cholesterol (286.6 mg/day vs 240 mg/day), thiamine (1.28 mg/day vs 1.13 mg/day) and niacin (16.90 mg/day vs 15.10 mg/day) than children with TD. A lower percentage of children with ASD in comparison with TD had an inadequate intake of vitamin E (85.30% vs 92.60%) and a higher intake of vitamin B12 (21.40% vs 10.80%). A higher percentage of children with subdiagnostic autistic symptoms than children with TD had an inadequate intake of magnesium (99% vs 78.6%). A large percentage of children in all age and diagnosis groups had a very high inadequate intake (80%–100%) of vitamin D and vitamin E and a high inadequate intake (50%–80%) of fibre, b-carotene (except the TD group) and magnesium. Primary school children had a high inadequate intake (50%–80%) of calcium and retinol and a moderately inadequate intake of iron (35%–48.4%).

Table 2.

Energy and nutrient intake and their inadequacy in preschool children by diagnosis.

	ASD^a (n = 36)			ASD Subdiagnostic^b (n = 21)			TD^c (n = 142)			p Intake adjusted	p % Children75/125
	IntakeMean (SD)[adjusted Mean]	% Children		IntakeMean (SD)[adjusted Mean]	% Children		IntakeMean (SD)[adjusted Mean]	% Children
	IntakeMean (SD)[adjusted Mean]	<75% EAR	>125% EAR	IntakeMean (SD)[adjusted Mean]	<75% EAR	>125% EAR	IntakeMean (SD)[adjusted Mean]	<75% EAR	>125% EAR
Energy (kcal/day){1719.1–2507.7}	1866.70 (443.5)[1864.33]	2.80	47.20	1835.40 (483.0)[1816.15]	0.00	33.30	1902.7 (523.7)[1906.18]	0.70	47.90	0.704	p = –/p = 0.760
Nutrients (units/day) in Mean (SD). {EAR}	IntakeMean (SD)[adjusted mean]	Inadequacy intake (%)[adjusted mean]		IntakeMean (SD)[adjusted mean]	Inadequacy intake (%)[adjusted mean]		IntakeMean (SD)[adjusted mean]	Inadequacy intake (%)[adjusted mean]		p Intake adjusted General(Subgroups^(a,b,c))	p Inadequacy intake adjusted General(Subgroups^(a,b,c))
Proteins (g/day){9.6–15.7}	57.60 (19.0)[57.55]	0.00		58.00 (18.7)[57.62]	0.00		58.50 (15.9)[58.57]	0.00		0.930	1.000
Carbohydrates (g/day)	200.00 (68.1)[199.60]	–		173.50 (69.9)[170.43]	–		191.00 (77.8)[191.57]	–		0.349	–
Starch (g/day)	69.10 (22.5)[69.00]	–		61.50 (14.5)[60.80]	–		64.90 (28.4)[65.06]	–		0.509	–
Free sugar (g/d)	63.30 (40.6)[63.09]	–		51.10 (33.3)[49.35]	–		57.50 (38.2)[57.82]	–		0.408	–
Natural sugar (g/day)	18.90 (8.9)[18.90]	–		18.10 (8.7)[18.23]	–		19.90 (7.5)[19.88]	–		0.585	–
Lipids (g/day)^‡	66.80 (12.2)[66.78]	–		68.50 (13.9)[68.13]	–		68.90 (13.6)[68.92]	–		0.683	–
SFA (g/day)	23.30 (5.2)[23.30]	–		23.40 (6.5)[23.27]	–		23.40 (6.2)[23.46]	–		0.984	–
PUFA (g/day)	7.80 (2.0)[7.77]	–		8.10 (1.7)[8.09]	–		7.90 (1.6)[7.91]	–		0.772	–
MUFA (g/day)	35.80 (7.58)[35.73]	–		39.30 (6.2)[39.11]	–		38.00 (5.9)[38.01]	–		0.080(0.049)^{a c}	–
Cholesterol (mg/day)	212.30 (66.6)[211.99]	–		238.10 (100.4)[235.30]	–		234.40 (81.6)[234.85]	–		0.303	–
Fibre (g/day){10–14}	12.90 (5.2)[12.84]	64.70[64.83]		11.9 (4.875)[11.77]	70.40[71.19]		13.10 (4.6)[13.10]	66.10[65.91]		0.487	0.821
Retinol (µg/day){245–320}	352.60 (126.7)[352.24]	15.50[15.88]		388.50 (136.2)[385.98]	11.20[11.02]		384.30 (142.1)[384.78]	12.50[12.53]		0.442	0.795
b-carotene (µg/day){1470–1920}	1648.40 (1851.7)[1646.74]	62.40[62.55]		1558.00 (894.2)[1544.60]	64.00[64.90]		1878.50 (903.0)[1880.93]	37.70[37.49]		0.297	0.001(0.003)^{a c} (0.009)^{b c}
Vitamin C (mg/day){25–40}	65.10 (31.7)[65.11]	4.90[4.89]		69.00 (35.5)[69.18]	4.00[4.03]		74.60 (31.6)[74.55]	0.29[0.29]		0.261	0.155
Vitamin D (µg/day){15}	1.47 (1.16)[1.47]	99.00[99.00]		1.91 (1.05)[1.92]	99.00[99.00]		2.11 (1.09)[2.11]	99.00[99.00]		0.009(0.002)^{a c}	1.000
Vitamin E (mg/day){9}	7.21 (2.22)[7.21]	86.40[86.52]		7.53 (1.27)[7.50]	81.80[82.57]		7.32 (1.22)[7.33]	85.40[85.22]		0.756	0.856
Thiamine (mg/day){0.40–0.48}	1.00 (0.29)[1.00]	0.00[0.00]		0.96 (0.29)[0.96]	2.30[2.29]		1.00 (0.30)[1.00]	0.00[0.00]		0.830	0.024(0.014)^{b c}
Riboflavin (mg/day){0.6–0.8}	1.49 (0.37)[1.49]	0.00[0.00]		1.48 (0.49)[1.47]	2.40[2.35]		1.51 (0.44)[1.51]	0.00[0.00]		0.880	0.032(0.018)^{b c}
Niacin (mg/day){7.3–8.7}	12.50 (4.4)[12.46]	10.80[9.69]		12.50 (4.3)[12.41]	19.00[17.42]		13.00 (4.2)[12.97]	7.10[6.23]		0.733	0.147(0.045)^{b c}
Vitamin B6 (mg/day){0.6–0.9}	1.34 (0.45)[1.34]	2.60[2.58]		1.28 (0.47)[1.27]	4.30[4.30]		1.35 (0.44)[1.36]	0.00[0.00]		0.732	0.055(0.037)^{a c} (0.012)^{b c}
Vitamin B12 (µg/day){1.5–2.5}	3.77 (1.26)[3.77]	5.10[5.12]		4.60 (1.67)[4.59]	1.50[1.54]		4.62 (1.58)[4.62]	0.00[0.00]		0.012(0.003)^{a c} (0.033)^{a b}	0.005(0.001)^{a c} (0.014)^{b c}
Folate (µg/day){110–160}	193.30 (70.0)[193.09]	5.10[5.10]		191.1 (69.3)[189.85]	9.50[9.60]		208.60 (72.0)[208.85]	3.30[3.24]		0.315	0.294
Calcium (mg){680}	772.30 (218.6)[771.87]	26.80[29.11]		747.10 (291.1)[743.78]	41.90[43.79]		760.60 (233.1)[761.22]	39.90[39.99]		0.911	0.305
Iron (mg/day){8}	8.80 (5.2)[8.78]	10.60[9.79]		7.60 (2.3)[7.51]	6.90[9.04]		8.20 (2.7)[8.17]	3.50[5.00]		0.340	0.388
Magnesium (mg/day){230}	221.40 (91.2)[221.15]	65.70[65.77]		192.10 (54.4)[190.41]	80.00[80.74]		201.90 (56.3)[202.18]	72.90[72.80]		0.159	0.459

ASD: autism spectrum disorder; TD: children with typical development; EAR: estimated average requirements; Inadequacy intake: probability of inadequate intake below EAR; SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid; MUFA: monounsaturated fatty acid.

[] = values adjusted by socioeconomic level.

^aAutism Spectrum Disorder; ^bSubdiagnostic autistic symptoms; ^cChildren with typical development: p-value ^(a,b,c) between subgroups of diagnosis.

Table 3.

Nutrient intake and their inadequacy in primary school children by diagnosis.

	ASD^a (n = 41)			ASD Subdiagnostic^b (n = 19)			TD^c (n = 191)			p Intake adjusted	p % Children75/125
	IntakeMean (SD)[adjusted mean]	% Children		IntakeMean (SD)[adjusted mean]	% Children		IntakeMean (SD)[adjusted mean]	% Children
	IntakeMean (SD)[adjusted mean]	<75% EAR	>125% EAR	IntakeMean (SD)[adjusted mean]	<75% EAR	>125% EAR	IntakeMean (SD)[adjusted mean]	<75% EAR	>125% EAR
Energy (kcal/day){1719.1–2507.7}	2125.80 (450.8)[2140.26]	9.80	21.90	2066.70 (293.1)[2013.33]	5.30	21.10	2210.80 (530.8)[2212.97]	3.70	31.90	0.210	p = 0.508/p = 0.889
Nutrients (units/day) in Mean (SD). {EAR}	IntakeMean (SD)[adjusted mean]	Inadequacy intake (%)[adjusted mean]		IntakeMean (SD)[adjusted mean]	Inadequacy intake (%)[adjusted mean]		IntakeMean (SD)[adjusted mean]	Inadequacy intake (%)[adjusted mean]		p IntakeadjustedGeneral(Subgroups ^(a,b,c))	p Inadequacy intake adjustedGeneral(Subgroups^(a,b,c))
Proteins (g/day){17.54–40.29 g/day}	73.10 (23.0)[73.27]	0.00		61.00 (11.5)[60.47]	0.00		65.50 (17.0)[65.47]	0.00		0.017(0.015)^{a c}	0.890
Carbohydrates (g/day)	211.20 (61.1)[213.46]	–		186.80 (45.6)[178.50]	–		207.80 (75.7)[208.21]	–		0.186	–
Starch (g/day)	95.10 (31.6)[95.51]	–		80.20 (34.1)[78.54]	–		89.40 (33.5)[89.44]	–		0.193	–
Free sugar (g/day)	61.70 (36.7)[62.94]	–		44.00 (21.7)[39.50]	–		53.00 (33.8)[53.15]	–		0.037	–
Natural sugar (g/day)	19.80 (8.1)[19.98]	–		22.30 (7.4)[21.66]	–		21.30 (10.3)[21.35]	–		0.701	–
Lipids (g/day)^‡	85.00 (24.0)[85.24]	–		78.60 (7.3)[77.49]	–		81.40 (13.1)[81.40]	–		0.154	–
SFA (g/day)	26.80 (9.0)[26.97]	–		24.20 (4.0)[23.70]	–		25.50 (5.8)[25.57]	–		0.172	–
PUFA (g/day)	10.20 (4.0)[10.24]	–		9.30 (1.1)[9.30]	–		9.50 (1.5)[9.54]	–		0.128	–
MUFA (g/day)	44.50 (11.5)[44.63]	–		46.10 (3.5)[45.62]	–		46.70 (5.2)[46.69]	–		0.172	–
Cholesterol (mg/day)	286.60 (113.4)[287.95]	–		243.40 (54.7)[238.65]	–		240.80 (68.5)[214.05]	–		0.002(0.001)^{a c}	–
Fibre (g/day){16–19}	14.60 (5.2)[14.68]	74.40[73.70]		14.40 (3.3)[14.01]	78.40[81.00]		15.20 (5.2)[15.18]	67.70[67.54]		0.573	0.273
Retinol (µg/day){320–480}	417.40 (522.5)[418.03]	66.30[65.30]		361.50 (75.4)[359.15]	63.20[65.59]		391.50 (122.6)[391.56]	54.70[54.59]		0.661	0.227
b-carotene (µg/day){1920–2880}	2015.80 (1989.2)[2005.00]	72.70[73.01]		1901.70 (741.8)[1941.68]	76.00[74.79]		2036.30 (1060.3)[2034.61]	67.20[67.22]		0.949	0.598
Vitamin C (mg/day){40–60}	74.20 (37.42)[74.79]	34.10[34.11]		87.70 (30.0)[85.62]	8.90[8.96]		84.80 (41.6)[84.92]	21.30[21.28]		0.333	0.050(0.034)^{a b}
Vitamin D (µg/day){15}	1.94 (1.41)[1.93]	99.00[99.00]		1.80 (0.97)[1.96]	99.00[99.00]		2.00 (1.08)[2.01]	99.00[99.00]		0.810	0.343
Vitamin E (mg/day){9–13}	8.93 (2.84)[8.93]	85.30[85.33]		8.90 (0.69)[8.90]	96.50[96.52]		8.99 (1.31)[8.99]	92.60[92.60]		0.962	0.040(0.029)^{a c}
Thiamine (mg/day){0.52–0.76}	1.28 (0.54)[1.29]	2.33[2.39]		1.06 (0.24)[1.05]	0.00[0.00]		1.13 (0.32)[1.13]	2.20[2.24]		0.022(0.014)^{a c}	0.639
Riboflavin (mg/day){0.8–1.1}	1.65 (0.56)[1.65]	13.90[14.11]		1.40 (0.27)[1.43]	9.20[8.58]		1.54 (0.45)[1.54]	9.80[9.80]		.203	0.639
Niacin (mg/day){9.4–13.7}	16.90 (6.5)[16.90]	23.00[21.26]		13.80 (3.6)[13.76]	17.0[12.83]		15.10 (4.6)[15.14]	21.10[17.36]		0.044(0.041)^{a c}	0.649
Vitamin B6 (mg/day){0.9–1.2}	1.63 (0.56)[1.64]	19.50[19.95]		1.50 (0.32)[1.44]	12.10[10.60]		1.55 (0.48)[1.55]	16.30[16.36]		0.317	0.591
Vitamin B12 (µg/day){2.5–3.5}	4.92 (3.76)[4.91]	21.40[21.41]		4.40 (0.93)[4.42]	8.20[7.95]		4.81 (1.45)[4.81]	10.80[10.85]		0.665	0.081(0.037)^{a c}
Folate (µg/day){160–210}	242.70 (89.7)[243.34]	30.30[30.58]		238.0 (52.8)[235.66]	17.90[16.65]		244.50 (82.0)[244.67]	26.90[26.98]		0.902	0.444
Calcium (mg){680–960}	745.30 (261.7)[747.89]	72.60[57.51]		695.40 (184.2)[685.90]	72.60[73.53]		754.70 (249.0)[755.04]	62.10[62.01]		0.518	0.402
Iron (mg/day){8}	9.40 (2.9)[9.48]	35.10[35.05]		8.30 (1.7)[8.11]	48.40[48.48]		9.00 (2.7)[9.03]	37.30[37.27]		0.191	0.485
Magnesium (mg/day){230–300}	229.80 (69.2)[230.73]	79.50[78.42]		205.20 (30.1)[201.94]	99.00[99.00]		221.70 (62.5)[221.80]	78.60[78.42]		0.257	0.032(0.007)^{b c}

[] = values adjusted by socioeconomic level.

Autism Spectrum Disorder; ^bSubdiagnostic autistic symptoms; ^cChildren with typical development: p-value ^(a,b,c) between subgroups of diagnosis.

Tables 4 and 5 show the percentage of energy provided by macronutrients and the percentage of children below or above the recommended values in each age group. Adequate protein and MUFA energy intakes were reported in all children. In general, all groups presented values lower than those recommended for carbohydrates and PUFAs and higher values than those recommended for added sugars, total fats and SFA. However, differences were observed between diagnostic groups. Preschool children with subdiagnostic autistic symptoms were further from the recommendations for carbohydrates and PUFAs than preschool children with ASD or TD. In relation to proteins, more children with ASD in both age groups had a lower protein intake than recommended, although this was less pronounced in the primary school group. Also, children with ASD had a greater percentage of energy from protein than TD. In both age groups, the lowest percentages of children with intakes below the recommendations for PUFAs and lower percentage of energy from MUFAs were children with ASD. Finally, primary school children with ASD had a lower energy intake from natural sugars, with the highest intake from added sugars (p = 0.054) than primary school children with TD.

Table 4.

Percentage of energy provided by macronutrients and their inadequacy in preschool children by diagnosis.

% Energy provided by macronutrients (units/day) in Mean (SD) {RD}	ASD^a (n = 36)			ASD Subdiagnostic^b (n = 21)			TD^c (n = 142)			P% EnergyadjustedGeneral(Subgroups ^(a,b,c))	P% childrenGeneral(Subgroups ^(a,b,c))
	% EnergyMean (SD)[adjusted mean]	% Children		% EnergyMean (SD)[adjusted mean]	% Children		% EnergyMean (SD)[adjusted mean]	% Children			P% childrenGeneral(Subgroups ^(a,b,c))
		<RD	>RD		<RD	>RD		<RD	>RD		<RD/>RD
Proteins (%){10%–30%}	14.30 (3.4)[14.29]	5.60	0.00	15.0 (2.6)[15.11]	0.00	0.00	14.70 (2.2)[14.66]	1.40	0.00	0.474	0.058/–(0.048)^{a c}/0.078
Carbohydrates (%){>50%}	48.20 (6.6)[48.21]	77.80	–	44.10 (7.6)[43.81]	85.70	–	46.00 (6.9)[46.02]	71.80	–	0.056(0.024)^{a b}	0.011/–(0.009)^{a b} /–
Starch (%)	16.90 (4.3)[16.89]	–	–	16.30 (3.2)[16.35]	–	–	15.90 (4.9)[15.86]	–	–	0.476	–/–
Free sugar (%) {<10%}	14.80 (6.9)[14.76]		75.00	12.60 (6.5)[12.39]		61.90	13.20 (5.8)[13.29]		73.20	0.280	–/0.315
Natural sugar (%)	4.80 (2.2)[4.79]	–	–	4.70 (1.8)[4.76]	–	–	5.10 (1.9)[5.07]	–	–	0.619	–/–
Fats (%){< 30 %}	37.50 (4.5)[32.73]	–	94.40	40.90 (5.9)[32.26]	–	90.50	39.30 (5.2)[32.26]	–	98.60	0.411	–/0.873
SFA (%) {<10%}	13.10 (2.3)[13.12]	–	88.90	13.90 (2.9)[13.90]	–	90.50	13.20 (1.9)[13.22]	–	95.80	0.354	–/0.110
PUFA (%) {7%–10%}	4.40 (1.0)[4.38]	97.20	0.00	4.80 (0.7)[4.86]	100.00	0.00	4.50 (0.7)[4.53]	99.30	0.00	0.074(0.040)^{b c}	0.969/0.927(0.048)^{a c}/–
MUFA (%) {>15%}	20.10 (3.9)[20.11]	2.80	–	23.60 (3.6)[23.74]	0.00	–	22.00 (3.9)[21.98]	0.70	–	0.002(0.011)^{a c} (0.001)^{a b} (0.048)^{b c}	0.104/–(0.007)^{a c}/–

ASD: autism spectrum disorder; TD: children with typical development; SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid; MUFA: monounsaturated fatty acid. RD: recommendations

[] = values adjusted by socioeconomic level

Autism Spectrum Disorder; ^bSubdiagnostic autistic symptoms; ^cChildren with typical development: p-value ^(a,b,c) between subgroups of diagnosis.

Table 5.

Percentage of energy provided by macronutrients and their inadequacy in primary school children by diagnosis.

% Energy provided by macronutrients (units/day) in Mean (SD) {RD}	ASD^a (n = 41)			ASD Subdiagnostic^b (n = 19)			TD^c (n = 191)			p % EnergyGeneral(Subgroups ^(a,b,c))	p % childrenGeneral(Subgroups ^(a,b,c))
	% EnergyMean (SD)	% Children		% EnergyMean (SD)	% Children		% EnergyMean (SD)	% Children			p % childrenGeneral(Subgroups ^(a,b,c))
		<RD	>RD		<RD	>RD		<RD	>RD		<RD/>RD
Proteins (%){10%–30%}	15.30 (3.0)[15.29]	2.40	0.00	14.40 (1.8)[14.58]	5.30	0.00	14.50 (1.84)[14.37]	1.00	0.00	0.033(0.010)^{a c}	0.701/0.029(0.011)^{a c}/–
Carbohydrates (%){>50 %}	44.20 (6.7)[44.38]	82.90	–	43.60 (4.6)[43.07]	89.50	–	44.50 (6.06)[44.54]	81.20	–	0.609	0.420/–
Starch (%)	20.20 (5.2)[20.13]	–	–	18.50 (5.9)[18.65]	–	–	19.40 (4.86)[19.39]	–	–	0.539	–/–
Free sugar (%) {<10%}	12.50 (6.0)[12.68]	–	63.40	10.10 (4.7)[9.55]	–	52.60	11.00 (4.77)[11.04]		53.90	0.054	0.406
Natural sugar (%)	4.30 (1.7)[4.29]	–	–	5.40 (2.2)[5.42]	–	–	4.70 (1.85)[4.66]	–	–	0.102	–/–
Fats (%) {<30 %}	40.40 (5.5)[45.97]	–	100.00	42.00 (4.2)[44.28]	–	100.00	41.10 (5.16)[45.00]	–	99.50	.167	–0.777/
SFA (%) {<10%}	12.60 (2.0)[12.60]	–	92.70	12.90 (2.0)[12.96]	–	94.70	12.70 (1.72)[12.76]	–	95.30	0.768	0.683
PUFA (%) {7%–10%}	4.80 (1.2)[4.80]	92.70	7.30	5.00 (0.5)[5.06]	100.00	0.00	4.80 (0.68)[4.83]	100.00	0.00	0.431	0.001(<0.001)^{a c} 0.039(0.014)^{a c}
MUFA (%) {>15%}	21.50 (4.3)[21.47]	0.00	–	24.70 (2.5)[24.91]	0.00	–	23.90 (4.03)[23.88]	0.00	–	0.001(0.001)^{a c} (0.021)^{a b}	<0.001/– (<0.001)^{a c} / –

ASD: autism spectrum disorder; TD: children with typical development; SFA: saturated fatty acid; PUFA: polyunsaturated fatty acid; MUFA: monounsaturated fatty acid. RD: recommendations.

Autism Spectrum Disorder; ^bSubdiagnostic autistic symptoms; ^cChildren with typical development: p-value ^(a,b,c) between subgroups of diagnosis.

Discussion

Our study provides new data on the energy, nutrient intake and nutritional adequacy comparing children with subdiagnostic autistic symptoms, ASD and TD. This study was conducted on two age groups – one group of preschool children aged from 3 to 6 years and one group of schoolchildren aged from 10 to 12 years – from the school population of a developed European Mediterranean country. The two age groups allowed us to observe characteristics in two evolutionary periods related to nutritional intake. A strong point of our study is that the participants came from a representative inclusive education school sample in a Spanish province, and all have a low to moderate level of ASD severity in agreement with ADOS-2 assessment.

Generally speaking, the energy and nutrient intake of children in our study regardless of the ASD diagnosis was similar to those of studies conducted with children of similar ages in other developed countries (Barnhill et al., 2018; Hyman et al., 2012; Johnson et al., 2008; Lockner et al., 2008). The nutritional patterns in all these societies meet or exceed the recommended energy and macronutrient intakes and present inadequate intakes of certain vitamins and minerals. The energy intake of the children in our study tended to be higher than those recommended for both age groups, since 33.3%–47.9% of preschool children and 21.1%–31.5% of primary school children consumed more than 125% of the recommended energy with no significant differences by diagnostic group.

With regard to nutrient intake, only few differences were observed between children with subdiagnostic autistic symptoms and ASD, which does not support the hypothesis that children with subdiagnostic autistic symptoms would be found between ASD and TD. Although they were somewhat more similar to ASD, the small size of this group made it difficult to observe significant differences. In addition, we are unaware of studies in the literature that include this diagnostic category, so the results of this group of children cannot be compared. It is important to note that although the nutritional status of children with ASD was worse than that of children with TD, the significant differences found in nutritional intake between children with ASD and children with TD in both age groups were very small, and their values are within the same nutritional range as the reference values, which indicates the low clinical relevance of these differences. Furthermore, since multiple comparisons have been made, it could be that some differences observed between groups may simply be random.

We have observed different food consumption patterns between the two age groups studied. While the preschool group with ASD, compared with the TD children, had a lower intake of MUFAs (35.8 g/day vs 38.0 g/day), vitamin D (1.47 µg/day vs 2.11 µg/day) and vitamin B12 (3.77 µg/day vs 4.62 µg/day) and a higher inadequate intake of b-carotene (62.4% vs 37.7%), vitamin B6 (2.6% vs 0%), vitamin B12 (5.1% vs 0%), the primary school children with ASD had a lower risk of nutritional deficiency than children with TD. For example, primary school children with ASD had a higher intake of protein (73.1 g/day vs 65.5 g/day), cholesterol (286.6 mg/day vs 240.0 mg/day), thiamine (1.28 mg/day vs 1.13 mg/day) and niacin (16.90 mg/day vs 15.10 mg/day) than primary school children with TD. These data contrast with both the lower protein intake described in the meta-analysis by Sharp et al. (2013) and the meta-analysis by our own research group (Esteban-Figuerola et al., 2019). It also diverges with the results of numerous other studies that have compared the intake of children (of similar ages) with ASD with that of children with TD, which reported lower protein intakes in children with ASD (Barnhill et al., 2018; Hyman et al., 2012; Malhi et al., 2017; Marí-Bauset et al., 2014; Munira et al., 2020; Neumeyer et al., 2018; Shmaya et al., 2014; Tsujiguchi et al., 2020) or similar protein intakes in children with ASD and children with TD (Al-Thbiany et al., 2017; Li et al., 2018; Lockner et al., 2008; Rithika & Sushma, 2019; Tomova et al., 2020; Xia et al., 2010). The high protein intake of children with ASD has been related to a higher prevalence of digestive disorders in this population (Sanctuary et al., 2018). Perhaps, this relationship could be mediated by changes in gut microbiota, and in this regard, Tomova et al. (2020) found higher abundance of Clostridia in children with ASD who preferred animal proteins.

In relation to the percentage of energy provided by macronutrients, we found an adequate contribution from proteins and MUFAs, which is typical of the Mediterranean area, in all age and diagnosis groups. Also, Tomova et al. (2020) in children from Slovakia found an adequate contribution of energy from proteins without differences in their samples with ASD and controls. However, an excessively high percentage of children presented an imbalance in energy intake from other macronutrients. In all groups, we observed a lower energy intake than recommended for carbohydrates and PUFAs and a higher energy intake than recommended for added sugars, total fats and AGS. Similarly, Tomova et al. (2020) found a higher percentage energy from fat (without differences between ASD and controls) but more appropriate percentage of energy from carbohydrates. Note that the potentially most damaging health imbalances in relation to added sugars, total fats and AGS affect practically all children (88.9%–100%). We also found very small differences indicative of poorer health in children with ASD, who present a lower percentage of energy from PUFAs (4.4% in preschool children and 4.8% in primary school children) and MUFAs (20.10% in preschool children and 21.59% in primary school children) than children with TD. Moreover, primary school children obtain a greater percentage of energy from proteins (15.30%) and added sugars (12.5%) (the WHO recommends that this contribution should be below 10%) than TD children. The increase in added sugars is associated with excessive energy intake and a low quality diet, which also caused the displacement of foods with higher nutritional value. Thus, this pattern of consumption is related to an increased risk of dental caries, obesity and other non-communicable diseases (WHO, 2015). Regarding fibre intake, it was observed that a large percentage of children in all age and diagnosis groups had a very high inadequate intake (50%–80%). Low fibre intake may alter the composition of the gut microbiota and worsen gastrointestinal functioning in children with ASD (Tomova et al., 2020).

In general, our data do not support, in comparison with children with TD, the lower intake of energy (Neumeyer et al., 2018; Zimmer et al., 2012), protein (Malhi et al., 2017; Marí-Bauset et al., 2014; Munira et al., 2020; Neumeyer et al., 2018; Shmaya et al., 2014; Tsujiguchi et al., 2020) or unbalanced energy (Meguid et al., 2017; Neumeyer et al., 2018) observed in children with ASD from samples selected at clinics or specialised schools, where subjects tend to have more severe profile of ASD symptoms. Regardless of the age or diagnosis, all the children in our study had a low intake of micronutrients, a very high risk of inadequate (80%) vitamin D and vitamin E intake, and a high risk (50%–80%) of inadequate b-carotene and magnesium intake. Inadequate calcium intake affected primary school children (72.6%–75.7%) more than preschoolers (26.8%–41.9%). Although food selectivity in children with ASD has previously been observed in this sample (Canals-Sans et al., 2022) and this may imply a higher risk of inadequate intake compared to children with TD (Jussila et al., 2020; Leader et al., 2020; Smith et al., 2020), our results suggest that the differences are only marginally significant. We detected a lower intake of vitamin D (1.47 µg/day vs 2.11 µg/day) and vitamin B12 (3.77 µg/day vs 4.62 µg/day) and a higher intake of b-carotene (62.4% vs 37.7%) in children with ASD than in children with TD. Other studies have also found higher deficiencies in children with ASD than in children with TD. Those studies often correspond to children with a higher degree of ASD severity (Attlee et al., 2015; Malhi et al., 2017; Marí-Bauset et al., 2015, 2016; Neumeyer et al., 2018; Shmaya et al., 2014; Tsujiguchi et al., 2020; Zimmer et al., 2012).

Comparison with nutritional recommendations was also analysed by Sharp et al. (2018) in a sample of children with ASD (2–12 years). They observed that nutritional intake was far from the recommendations for vitamins D, E and A, zinc, folic acid, calcium and fibre. This risk of deficient intake is similar to those observed in our study: vitamins D, E, and A, zinc, folic acid, calcium and fibre. Vitamin D has an important function at the cellular, organic, cognitive and bone levels. Vitamin D deficiencies contribute to hypocalcaemia and can therefore affect bone mass development. This impairment may be greater in children with ASD, as observed by Molloy et al. (2010), where children with ASD reported lower bone density than their peers. Also related to vitamin D, we found a highly inadequate intake of calcium in primary school children (62.1%–72.6%), which may also have an adverse effect on bone health. Low levels of calcium can increase the risk of osteomalacia and osteoporosis due to its important role in bone osteogenesis during childhood. Guo et al. (2020) suggested that calcium deficiency may be associated with the severity of social impairment in children with ASD. Moreover, some authors have found that adequate levels of calcium and iron are correlated with appropriate cognitive and neurological function and development in children with ASD. Other studies have also found in children with ASD compared to children with TD higher deficient intakes of vitamin D (Al-Thbiany et al., 2017; Attlee et al., 2015; Barnhill et al., 2018; Hyman et al., 2012; Lockner et al., 2008; Marí-Bauset et al., 2015; Tsujiguchi et al., 2020) and calcium (Barnhill et al., 2018; Herndon et al., 2009; Hyman et al., 2012; Marí-Bauset et al., 2015, 2016; Tsujiguchi et al., 2020; Vanuza & Cordeiro, 2018; Xia et al., 2010). Over 63% of our sample had an inadequate intake of vitamin A (b-carotene and retinol), which plays an important role in growth, vision and the central nervous system. In children with ASD, some authors have also described lower vitamin A intakes (Hyman et al., 2012; Lockner et al., 2008; Tsujiguchi et al., 2020; Vanuza & Cordeiro, 2018), which can exacerbate symptomatology and behavioural problems (Guo et al., 2020). Vitamins C and E play important roles as antioxidants, preventing oxidative stress and performing a wide range of physiological functions in the human body. At-risk intakes of these vitamins have been also observed in other studies (Attlee et al., 2015; Hyman et al., 2012; Malhi et al., 2017; Tomova et al., 2020; Tsujiguchi et al., 2020; Xia et al., 2010), including vitamin E (Barnhill et al., 2018; Herndon et al., 2009; Hyman et al., 2012; Lockner et al., 2008; Marí-Bauset et al., 2015; Xia et al., 2010). Magnesium deficiencies, that were also found in Meguid et al. (2017), Tsujiguchi et al. (2020), Xia et al. (2010) and Zimmer et al. (2012), may cause irritability, excitability and noise disturbance that could exacerbate ASD symptoms, although these relationships are still under investigation.

In general, the results of previous studies illustrate the lack of consensus on the definition of differences in the intake of vitamins and minerals in children with ASD compared to children with TD. This may be related to several factors, including the characteristics of the sample, that is, the children’s ages and social/family environment. Diet is widely recognised as being closely linked to the socioeconomic, cultural and religious characteristics of the social environment and to family habits and relationships. The differences in diet between children with ASD and children without it may also depend on the severity of the disorder. Our sample from the general school population comprised children with subdiagnostic autistic symptoms and less severe ASD than the children from clinics or special education schools participating in other studies. Children with more severe ASD tend to have more clinical characteristics associated with adherence to sameness, sensory particularities, gastrointestinal disorders and psychopathological comorbidities, which may influence their eating behaviours and, therefore, their nutritional status.

Although the differences in micronutrient intakes between the various groups are few, they describe a pattern of intake in children with ASD that is somewhat different from that of children with TD. Children with ASD scored worse on the diet quality index, had higher BMIs and reported more cases of obesity (a trend in preschool children) than children with TD. Primary school children with ASD had a higher intake of protein and cholesterol and a lower intake of vitamin D and b-carotenes than those with TD. Both age groups of children with ASD had a lower percentage of energy from MUFAs. Also, children with ASD and subdiagnostic autistic symptoms showed somewhat lower levels of physical activity (Esteban et al., 2021). All of this, added to other inadequate intakes they share with children with TD, describe a slightly deficient nutritional pattern that is associated with a greater risk of future cardiovascular diseases, obesity, type 2 diabetes mellitus, osteoporosis and so on (Luding et al., 2018; Makarem et al., 2018; WHO, 2015).

A strong point in our study is that the participants came from a representative school sample in a Spanish province. Thus, profiles of different severity ranges were considered, including milder ASD cases that are often overlooked in clinical samples. This allowed us to include children with ASD, children with subdiagnostic autistic symptoms and children with TD of the same ages and from the same geographical area, avoiding biases associated with samples from different sources. However, the number of participants with severe profiles of ASD and subdiagnostic autistic symptoms was low, which limited our ability to generalise the results from these groups. From the methodological perspective, the ASD diagnostic procedure was highly rigorous, and the questionnaires used were validated. Although 56% participation in population-based studies may be considered adequate, the possible difference in consumption patterns between participating and non-participating children should also be recognised as a limitation. The FFQ has less validity than reference methods that assess food consumption over several says (repeated 24-h recall or food diaries). However, is widely used for large samples in epidemiological studies given it makes it easier to record the habitual food consumption of subjects (Shim et al., 2014). Specifically, the FFQ used was validated with 24-h recall (6–9 days of diet records), obtaining a high correlation coefficient in the same population as that of our study. Therefore, we have considered using the FFQ a good method to obtain reliable results. Given the large number of comparisons, some significances could be random, even applying the Bonferroni correction. There could also be differences between diagnostic groups that have not been detected in this study (type 2 error).

Conclusion

The nutritional intake of children with ASD differed little from that of TD children. All the groups in the sample had insufficient and very inadequate intakes (>80% or a very similar value) of vitamin D, vitamin E, b-carotene, magnesium and fibre; calcium deficiency was high only in the primary school group. The energy intake provided by carbohydrates and polyunsaturated fatty acids was lower than the recommended intake in more than 80% of the children (or a very similar value), whereas that from fats, saturated fatty acids and added sugars was much higher. The few differences between children with ASD and children with TD may be related to the low level of ASD severity in our ASD sample. Also, studies on larger samples of children with subdiagnostic autistic symptoms are needed to draw conclusions about the nutritional profile of this group.

Footnotes

Acknowledgements

The authors are grateful to the URV English Service for the review of the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical approval

This study was approved by the Research and Ethics Committee of the Sant Joan University Hospital (13-10-31/10proj5) and by the Catalan Department of Education.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This study was supported by Spain’s Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF) (grant PSI2015-64837-P); by the Catalan Government’s Department of Universities, Research and Information Society and the European Social Fund (FSE) (grant 2019FI_B2_00148); and by Spain’s Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF) (Project RTI2018-097124-B-I00).

Study design and sample

The study: Neurodevelopmental Disorders Epidemiological Research Project (EPINED).

ORCID iDs

Patricia Esteban-Figuerola

Paula Morales-Hidalgo

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