Research Paper On Autism And Nutrition

Introduction

Autism spectrum disorder (ASD) is a developmental disorder typified by impaired communication and social skills (Grabrucker, 2012). A recent increase in cases of ASD from 4–5 of 10,000 persons in 1966 to 100 cases of 10,000 persons currently (Fombonne, 2009) may not solely be explained by genetic factors (Abrahams and Geschwind, 2010). Thus, it needs to be determined whether environmental factors play a role in the onset of ASD (Grabrucker, 2012), and a recent study using twin samples reported that around 50% of cases of ASD can be explained by environmental factors (Hallmayer et al., 2011).

In the present mini-review, we report several relatively new studies that have evaluated the association between ASD and environmental factors by focusing on chemical or nutritional exposures because these are modifiable factors. These exposures included smoking/tobacco, alcohol, air pollution, pesticides, endocrine-disrupting chemicals, heavy metals, micronutrients, and fatty acid. Parental obesity was also included as an exposure because maternal obesity can be an indicator of exposure to chemicals or nutrition.

Smoke or Tobacco

Although not consistent, most recent population-based studies have suggested that maternal smoking during pregnancy is not directly associated with ASD after adjusting for socioeconomic status (Burstyn et al., 2010; Kalkbrenner et al., 2012; Lee et al., 2012; Tran et al., 2013). For example, Lee et al. (2012) performed a population-based nested case-control study of 3958 cases of ASD and 38,983 controls in a longitudinal register-based study consisting of individuals aged 4–17 years, and found that maternal smoking during 8–12 weeks of gestation was significantly associated with an increased odds of high-functioning autism in an unadjusted model (odds ratio [OR] = 1.22, 95% confidence interval [CI]: 1.09, 1.36); however, this finding was no longer statistically significant after adjusting for parental socioeconomic status. Additionally, Tran et al. (2013) conducted a population-based nested case-control study comprising 16,185 samples, including 4020 cases of ASD, based on the Finnish National Birth Cohort. Maternal smoking during all pregnancies was not associated with the offsprings' ASD status after adjusting for confounding factors.

Considering that these studies were conducted mostly among Caucasians, the impact of smoking on the development of ASD may differ by race. Zhang et al. (2010) conducted a case-control study using 190 Han children aged 3–21 years with and without autism in China, and found that maternal second-hand smoke exposure during pregnancy, was significantly associated with autism (OR = 3.53, 95% CI: 1.30, 9.56), suggesting that maternal smoking may be associated with ASD among Asians.

Alcohol

Few studies have evaluated the impact of maternal alcohol use on the onset of ASD among offspring. Two population-based nested control studies in North European countries reported that maternal alcohol intake during pregnancy was not associated with ASD (Daniels et al., 2008; Eliasen et al., 2010).

Air Pollution

In the last decade, literature on the effect of air pollution exposure during pregnancy on the risk of ASD has grown immensely. Although a large study using direct person-based air sampling is needed, the analytical models used to calculate residence-based effects have become increasingly complex to correctly estimate exposure during a specific time. Regardless of this change in measuring the effect of exposures, most studies have shown a positive association between air pollution exposure and ASD (Suades-Gonzalez et al., 2015). Recent findings support that exposure to particulate matter (PM) < 2.5 μm in diameter (PM2.5) during the third trimester causes the most detrimental effect on the development of ASD (Kalkbrenner et al., 2015; Raz et al., 2015; Talbott et al., 2015; Weisskopf et al., 2015).

One of the earlier studies that measured air pollution during pregnancy was conducted in 2011 using the distance of one's residence to major roadways as its proxy. Comparing 304 cases of ASD and 259 controls, Volk et al. (2011) reported that mothers of children with ASD were more likely to have lived near a freeway during their third trimester (OR 2.22, CI: 1.16, 4.42) or at the time of delivery (OR 1.86, CI: 1.04, 4.42). This study raised another research question: which type of air pollution has the most effect on the onset of ASD? Evidence of the effect of ozone or nitro-oxides on ASD has been inconclusive (Becerra et al., 2013; Gong T. et al., 2014; Guxens et al., 2016), but maternal exposure to small particles such as diesel PM (Windham et al., 2006; Roberts et al., 2013), PM 2.5 (Becerra et al., 2013; Volk et al., 2013; Raz et al., 2015; Talbott et al., 2015), and PM < 10 μm in diameter (PM10) (Volk et al., 2013; Kalkbrenner et al., 2015) has been most consistently reported with an increased risk of ASD. Several case-control studies have investigated the effect of individual hazardous air pollutants (HAP) such as metals and volatile organics on ASD (Windham et al., 2006; Kalkbrenner et al., 2010; Roberts et al., 2013; von Ehrenstein et al., 2014) with all studies conducted in the United States showing that cases of ASD have an elevated exposure of HAP by 1.3–2.0 times. However, two reports from European countries (Guxens et al., 2016) and a separate twin study in Sweden (Gong T. et al., 2014) showed no association of maternal exposure to air pollution and ASD.

Most studies about the effect of air pollution on ASD have been prone to residual confounders such as a low socioeconomic status, which is related to both worse living environments and an increased risk of ASD (Bell and Ebisu, 2012; Shmool et al., 2014).

Pesticides

Evidence from previous studies has suggested a strong relationship between pesticide exposure and ASD. Despite the quick turnover in commercial product names, organophosphates (OP) and organochlorines (OC) are still in use despite their neurotoxicity (Kalkbrenner et al., 2014). The association between ASD and pesticides has been observed across studies that measured exposures from residential exposure to agricultural drift (Roberts et al., 2007; Roberts and English, 2013), administered questionnaires on the use of insecticides (Keil et al., 2014), and assessed bio-specimens to detect metabolites (Rauh et al., 2006; Eskenazi et al., 2007; Cheslack-Postava et al., 2013) and numerous pesticides, including but not limited to OC (Roberts et al., 2007; Cheslack-Postava et al., 2013; Roberts and English, 2013; Braun et al., 2014) and OP (Rauh et al., 2006; Eskenazi et al., 2007; Shelton et al., 2014) pesticides.

Shelton et al. (2014) compared 486 cases of ASD and 316 controls, and found an association with OP exposure and ASD, which strengthened later in pregnancy for mothers living within 1.75 km from the agricultural use of OP during their third trimester. They also found increased exposure to pyrethroids in patients with ASD. Eskenazi et al. (2007) and Rauh et al. (2006) reported that cases of ASD had higher OP metabolites during early- to mid-pregnancy. Other case-control studies reported that exposure to imidacloprid through the consistent use of flea/tick pet treatment throughout pregnancy period was associated with ASD (Kalkbrenner et al., 2014; Keil et al., 2014).

Endocrine-disrupting Chemicals

Although, polychlorinated biphenyl (PCB) and several dioxins such as tetrachlorodibenzodioxin were banned by the Stockholm Convention in 2001, they are still detected in humans due to their long half-life in the environment, as well as the consumption of predatory fish in which such chemicals tend to accumulate. Other chemicals are still used, such as bisphenol A (BPA), in many canned foods, receipts, toys, and medical equipment, and some chemicals such as polybrominated diphenyl ethers and phthalates may have even increased body burden (Zota et al., 2008, 2014). Studies on these chemicals are sparse with mixed findings.

Associations between ASD and PCB are inconsistent (Kim et al., 2010; Cheslack-Postava et al., 2013; Braun et al., 2014), and the seemingly elevated risks in a pilot study (Cheslack-Postava et al., 2013) have been criticized for possible bias due to lack of adjustment for birth order (Kalkbrenner et al., 2014). Kardas et al. reported higher serum BPA concentrations in a case-control study of 48 cases of ASD and 41 controls, but no measurement of prenatal exposure was reported (Kardas et al., 2016). Braun et al. (2014) and Miodovnik et al. (2011) failed to find any association with the score of Social Responsiveness Scale (SRS), measurement of ASD traits, and maternal BPA serum or urine concentration and in their cohort studies; however, Braun et al. (2009) found that mid-pregnancy BPA concentrations were associated with an increase in externalizing problem behaviors in early childhood.

However, studies on phthalates mostly suggest an association between ASD and phthalates. Miodovnik et al. (2011) studied 137 children and found that higher phthalate metabolites in maternal urine in the third trimester were associated with a lower score on several of the SRS subscales at 7–9 years old (Miodovnik et al., 2011). Larsson et al. (2009) followed 4779 children and reported that those at 1–6 years old living in homes with polyvinyl chloride flooring (a significant source of phthalates) were 2.4 times more likely to be diagnosed with ASD (Larsson et al., 2009). Kardas et al. (2016) also reported higher serum phthalates concentrations in cases of ASD (Kardas et al., 2016). Braun et al. (2014) failed to detect an association between phthalates in maternal urine and ASD, and Phillipat et al. (Philippat et al., 2015) also failed to detect an association between house dust levels of phthalates and ASD; however, Phillipat et al. explained that the lack of association may be due to fact that the measured exposure may have only poorly reflected the actual exposure of phthalates.

Heavy Metals

There is sufficient evidence that maternal exposure to heavy metals such as lead, mercury, cadmium, and arsenic cause an increase in neurodevelopmental disorders, and restrict fetal and infant growth even at low-level exposures (De Palma et al., 2012; Ornoy et al., 2015). However, less research has been conducted on heavy metals in relation to ASD. Recently, Rossignol et al. (2014) systematically reviewed literature on environmental toxicants and summarized 40 case-control studies that compared a variety of heavy metal concentrations (i.e., lead, mercury, arsenic, cadmium, aluminum, fluoride, manganese, chromium, nickel, uranium, and tin) in blood, hair, brain, teeth, or urine in children with ASD compared to controls, as well as seven similar studies on urinary porphyrin, which is considered to have a heavy metal burden (Rossignol et al., 2014). The most studied metals were mercury (29 studies) and lead (25 studies). Although the urinary porphyrin studies collectively suggest a higher heavy metal burden among children with ASD, a recent study by Dickerson et al. (2015) found that among 2489 children the prevalence of ASD was higher when mothers were living closer to industrial facilities that released arsenic, lead, or mercury.

A meta-analysis of seven studies on the mean hair level of mercury in a total of 343 cases of ASD and 317 controls did not show any significant association between mercury and ASD (De Palma et al., 2012) and neither did a recent cohort study by van Wijngaarden et al. (2013) on 1784 children and young adults. Some studies that assessed blood have found an association between mercury and ASD (Ip et al., 2004; Desoto and Hitlan, 2007; Geier et al., 2010), whereas others have not (Hertz-Picciotto et al., 2010; Stamova et al., 2011; Albizzati et al., 2012; Adams et al., 2013; Rahbar et al., 2013). However, the lack of adjusting for strong protective factors such as fish oil that are ingested concomitantly in many of the studies (Karagas et al., 2012) and the possible conflict of interest with industries (Kern et al., 2015) may be masking existing associations, as studies on air-borne mercury consistently report an association between mercury exposure and ASD (Windham et al., 2006; Roberts et al., 2013).

Micronutrients

Micronutrients are essential for neurogenesis and the development of the neuro-network (Curtis and Patel, 2008). Lower levels of magnesium (Strambi et al., 2006), zinc (Adams and Vogelaar, 2005), selenium (Adams and Vogelaar, 2005), vitamin A (Adams and Vogelaar, 2005), vitamin B complex (Adams and Vogelaar, 2005; Pineles et al., 2010), vitamin D (Adams and Vogelaar, 2005; Gong Z. L. et al., 2014; Kocovska et al., 2014), vitamin E (Adams and Vogelaar, 2005), and carnitine (Filipek et al., 2004) in blood, hair, or other tissue among children with ASD have been reported. Further, the association between a deficiency of micronutrients during pregnancy, such as folic acid (Schmidt et al., 2011, 2012; Suren et al., 2013) and vitamin D (Cannell, 2008; Grant and Soles, 2009), have been reported as a risk for offspring developing ASD.

These previous studies advanced to intervention studies to confirm the causality or possibility of using nutrients to treat ASD. Several studies have reported that nutritional intervention showed a trend toward improvement in patients with ASD. For example, a double-blind study on 20 children (age 3–8 years) with ASD who took a broad-based multi-vitamin and mineral supplement suggested the possible benefit of improving general behavior and receptive language, although this finding was not significant (Adams and Holloway, 2004). Another double-blind study reported that supplementing L-carnosine to children (age 3–12 years) with ASD showed statistically significant improvements in the symptoms on ASD (Chez et al., 2002). In another study, it was also reported that oral magnesium and vitamin B6 supplements led to improvements in social interactions, communication, stereotyped restricted behavior, and abnormal/delayed functioning among children (age 1–10 years) with ASD (Mousain-Bosc et al., 2006).

Several studies have reported the association between gender and ASD in the relationship with micronutrients. For example, a study conducted in the Faroe Islands (Kocovska et al., 2014) noted the trend for ASD males having lower levels of vitamin D and 25(OH)D3. Similarly, another study suggested that the differenteial effects of estrogen and testosterone on vitamin D metabolism might explain the gender difference of ASD (Cannell, 2008).

Fatty Acids

As neural development requires essential fatty acids, particularly long-chain omega-3 fatty acids during critical growth periods, and inflammation may be associated with ASD (Ornoy et al., 2015), the fatty acid level may play an important role in the development of ASD. Several studies have shown that both red blood cell and plasma fatty acid composition among cases of ASD differ from those of non-ASD people. Specifically, the levels of omega-3 fatty acids (Vancassel et al., 2001; Bell et al., 2004; Brigandi et al., 2015), docosahexaenoic acid (DHA) (Meguid et al., 2008; Wiest et al., 2009; El-Ansary et al., 2011; Al-Farsi et al., 2013; Brigandi et al., 2015), and arachidonic acid (AA) (Meguid et al., 2008; El-Ansary et al., 2011; Brigandi et al., 2015; Yui et al., 2016) were significantly lower in the red blood cell or plasma of cases of ASD compared to controls, although some studies did not support these claims (Bu et al., 2006; Bell et al., 2010). To date, only one study has examined maternal fatty acid intake during pregnancy is association with ASD (Lyall et al., 2013). Women with higher intake of polyunsaturated fatty acids (PUFA) before and during pregnancy had a reduced risk of having a child with ASD than those with lower PUFA intake. Analysis on specific PUFAs showed that women in the highest quartile of intake of omega-6 fatty acids had a 34% reduction in the risk of having a child with ASD compared with those in the lowest quartile, with similar results for linoleic acid intake. In concern with omega-3 fatty acids, women with very low intakes (i.e., the lowest 5% of the distribution) of had a significantly increased risk of having a child with ASD compared with those in the middle 90% of the distribution.

Reports on the benefits of fatty acid supplementation in children with ASD are inconclusive. Recently, Mankad et al. (2015) conducted a randomized controlled 6-month trial of 1.5 g/day of omega-3 fatty acids or a placebo in 38 children aged 2–5 years with ASD, and found no evidence for the efficacy of omega-3 fatty acids on improving core symptoms (Mankad et al., 2015). However, Ooi et al. (2015) conducted a 12-week open-label study of 1 g/day of omega-3 fatty acids in 41 children aged 7–18 years with ASD, and found significant improvements in the core symptoms and attention problems (Ooi et al., 2015). Yui et al. (2012) conducted a randomized controlled 16-week trial of AA and DHA supplementation or a placebo in 13 individuals aged 6–28 years with autism, and found significant improvements in social withdrawal and communication (Yui et al., 2012). These studies were relatively small, thus the findings may be by coincidental so a further larger randomized controlled trial is needed.

Parental Obesity

Maternal obesity can be associated with having offspring with ASD due to the accumulation of the aforementioned chemicals, or it can serve as a proxy of poor nutrition (Dodds et al., 2011; Kawicka and Regulska-Ilow, 2013; Ornoy et al., 2015). According to a Swedish cohort study of 333,057 participants, which included 6420 individuals with ASD, maternal overweight or obesity evaluated at the first antenatal visit was associated with having an offspring with ASD (Gardner et al., 2015). However, the association between an elevated maternal body mass index and the risk of ASD was not clear in matched sibling analyses.

In a population-based prospective cohort study of 92,909 children (age 4–13 years), Suren et al. (2014) investigated the association between ASD and paternal obesity recorded in the questionnaires answered by the fathers. They found that paternal obesity was associated with an increasing risk of ASD (adjusted OR: 1.73, 95% CI: 1.07, 2.82), whereas maternal obesity showed only a weak association with ASD (Suren et al., 2014).

Summary and Future Directions

In summary, several chemical exposures such as air pollution (e.g., PM 2.5), pesticides, BPA, phthalates, mercury or lead, and nutrition deficiencies such as folic acid, vitamin D, or fatty acid are possibly associated with the onset of ASD, whereas other traditional risk factors such as smoke/tobacco, alcohol, or PCB are less likely to be associated with ASD. Apparently, no single environmental factor can explain the development of ASD, suggesting that upstream environmental factors such as socioeconomic status need to be considered as risk factors for ASD, which have not been as rigorously investigated (Fujiwara, 2014). Further, few studies have investigated the accumulative or synergistic effect of the different chemical exposures and nutrition deficiencies simultaneously. The impact of multiple exposures to chemicals and nutrient deficiencies, which are suggestive of association with ASD, need to be studied together to assess whether effect is additive or multiplicative. Moreover, not all children exposed to these chemicals or nutrients may have risk of developing ASD, suggesting that some genetic polymorphism related to ASD, such as CD38 (Higashida et al., 2012), may have an interaction effect with these environmental exposures during the onset of ASD, as studied in the exposure of heavy mental and genetic polymorphism related to metabolism (Rossignol et al., 2014). Moreover, few chemical or nutritional exposures were investigated to elucidate the mechanism of gender difference of ASD prevalence. These uncovered topics need to be investigated in future research.

Author Contributions

TF conceived the review focus, conducted literature review, summarized, and finalized the manuscript. NM, YH, MS, and YT reviewed literature, wrote first draft, and finalized the manuscript. All authors approved final version of manuscript.

Funding

This study was supported from Japan Agency for Medical Research Development (16gk0110001h0003).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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ABSTRACT

The incidence of individuals with autism spectrum disorders (ASDs) is on the rise; therefore, well-timed screening is important. Given that this is a nutritionally vulnerable population, it is imperative to conduct a detailed nutritional assessment so that timely and intensive interventions can be recommended. This review article summarizes the research, focusing on the nutritional status of individuals with ASDs based on their anthropometric measurements, biomarkers, and dietary assessments. Research examining anthropometric measurements reveals an abnormally accelerated rate of growth among children with autism but shows inconsistent findings on the prevalence of overweight/obesity in comparison with typically growing children. Although dysregulated amino acid metabolism, increased homocysteine, and decreased folate, vitamins B-6 and B-12, and vitamin D concentrations have been proposed as possible biomarkers for an early diagnosis of ASDs, research investigating their association with age, gender, severity, and other comorbid psychiatric/nonpsychiatric disorders is lacking. There is consensus that children with autism have selective eating patterns, food neophobia, limited food repertoire, and sensory issues. Although inadequate micronutrient but adequate macronutrient intakes are increasingly reported, there are inconsistent results about the extent and type of nutrient deficiencies. Identification and development of nutritional assessment indicators that serve as early warning signs during routine practice beginning at birth and extending throughout the child's growth are necessary. With this population aging, there is also a dire need to study the adult population. A more vigorous role by nutrition professionals is warranted because management of potential comorbidities and contributory factors may be particularly problematic.

anthropometry, biochemical assessment, dietary assessment, nutritional assessment, autism spectrum disorders, nutritional status

Introduction

Autism or autistic spectrum disorder (ASD)3 is a wide-spectrum neurodevelopmental disorder encompassing impairments in social interaction, language, communication, and imaginative play. It also includes restricted, repetitive, and stereotyped patterns of behaviors, activities, and interests. Recent estimates are that 1 in 88 children suffer from ASDs, indicating a 78% increase from 2002, and are almost 5 times more common among boys (1 in 54) than girls (1 in 252) (1). Multiple genetic, environmental, and immunologic factors play a role in its pathogenesis. Increased awareness and improved diagnostic criteria may also be attributed to this jump rather than new environmental influences.

Research highlights that individuals with ASDs are nutritionally vulnerable because they exhibit a selective or picky eating pattern and sensory sensitivity that predisposes them to restricted intakes (2–8). This is further attenuated by dietary restrictions (such as gluten-free or casein-free diets) imposed by parents/caretakers as a therapeutic tool with the goal of improving behavior and/or gastrointestinal symptoms (9). Whether these individuals present malnutrition similarly or differently or more frequently than typical individuals is inconclusive. Because an individual's nutritional status is a result of complex mechanisms and interactions, a detailed nutritional assessment by a registered dietitian is essential for developing guidelines specific for individuals with ASDs. The purpose of this review article, therefore, was to summarize the pertinent information regarding the nutritional status of this complex behavioral disorder.

Methods

Nutritional assessment by a registered dietitian often includes evaluation across 5 different domains popularly known as the anthropometry, biochemical, clinical, dietary, and environmental approach. Although anthropometry allows the measurement of body size, composition, weight, and proportions, the biochemical assessment involves measuring nutritional markers and indicators of organ function in biological specimens (blood, urine, feces, hair, nails, and tissue samples). The nutrition-focused clinical (also known as physical) exam assesses the patient for signs and symptoms consistent with malnutrition or specific nutrient deficiencies through inspection, palpation, percussion, and auscultation. The dietary assessment identifies the patient's usual pattern of intake, food preferences (including ethnic, cultural, and religious influences), and use of alcohol, complementary and alternative medicine, and vitamin/mineral/herbal supplements (10). Finally, environmental factors such as socioeconomic status, social support systems, lifestyle, and social interactions affect nutritional status and are integral to nutritional assessment.

For the purpose of this review, research focusing on anthropometric, biochemical, and dietary assessment was studied. A literature search was performed using the electronic database PubMed to identify relevant research studies published in English during the past decade (since 2000). The search term combinations used were a population term (e.g., autism and autism spectrum disorder) and either an anthropometric term [e.g., height, weight, head circumference (HC), obesity, and overweight], a biochemical term (e.g., biochemical status, blood concentrations, and biomarkers), or a food-related term (e.g., food, feeding, diet, diet therapy, eating, nutrient, and nutrition). The lists of the articles obtained were manually searched for additional references, and all of the relevant studies have been included in this review under 3 broad categories: anthropometric, biochemical, and dietary assessment.

Results/Current Status of Knowledge

Anthropometric assessment

Researchers have studied the anthropometric measurements, mainly height, weight, BMI, and HC, of children with autism and compared them either with typically developing controls or with a reference healthy population or those with other psychiatric conditions (Table 1). Findings on the prevalence of obesity have been quite inconsistent (2, 11), although some studies (12, 13) reported a somewhat similar prevalence of overweight (19% compared with 16%)/obesity (30.4% compared with 23.6%) between autistic individuals and individuals without autism; other studies (14–17) reported a higher prevalence of overweight among children with ASDs (13–20%). With increasing age, children with autism have shown a trend toward an increasing prevalence of underweight (17, 18) or overweight [2- to 5-y-old children compared with 6- to 11-y-old children: 14.2% compared with 50% (13) and 31.8% compared with 37.9%, respectively (15)] and were twice as likely to be obese as children without autism (12, 18). Nonetheless, overweight and obesity are as important a concern in children with autism as in the general population (12, 13), and their unusual dietary patterns and decreased access to opportunities for physical activity may be contributory factors.

TABLE 1

A summary of studies highlighting the anthropometrics of individuals with ASDs1

Study Population Group Sample size, nAge,2Finding (ASD vs. control) 
Retrospective chart review 
 Egan et al. (14) United States Children with ASDs 169 3.89 ± 0.1 Prevalence of overweight: 17.2% vs. 12.5% 
Children with Asperger disorder/PDD-NOS 104 Prevalence of obesity: 21.9% vs. 10.6% 
 Grandgeorge et al. (30) France Children with ASDs 422 Birth HC, body length, and weight: similar at birth 
Typically developing children 153 Macrocephaly* 
 Fukumoto et al. (26) Japan Infants with autism 85 Birth–1 HC (boys): similar at birth, 6 mo,* and 12 mo** 
Control children 14,115 
 Mraz et al. (25) United States Children with ASDs 35 Birth–2 HC: birth–2 wk** and 10–14 mo* 
Healthy infants 37 Height/weight: 1–2 mo* 
National normative data (CDC) 
 Hazlett et al. (23) United States Children with autism 113 Birth–3 HC: normal at birth and 12 mo* 
Control children 189 
 Courchesne et al. (21) United States Children with ASDs 48 Birth–1 HC: birth** and 6–14 mo* (increased from 25th to 84th percentile) 
Cross-sectional study 
 Curtin et al. (12) United States Children from NSCH (2003–2004) 85,272 3–17 Prevalence of obesity: 30.4% vs. 23.6%. 
 Bicer and Alsaffar (2) Turkey Children with autism 164 4–18 Prevalence of overweight/obesity: 58.5% 
 Suren et al. (20) Norway Children with ASDs 376 Birth–3 HC, weight, and length: similar at birth 
Population cohort 106,082 By 12 mo of age: boys, HC similar but greater variability, longer (by 1.1 cm), and heavier (by 300 g); girls, reduced HC (by 0.5 cm), similar length, and lighter (by 150–350 g) 
 Hyman et al. (17) United States Children with ASDs 36732–11 2- to 5-y olds: overweight/obese* 
Controls (NHANES, 2007–2008) 559 6- to 11-y olds: underweight* 
 Xiong et al. (15) China Children with autism 429 2–11 2- to 5-y olds vs. 6- to 11-y olds: at risk of obesity (31.8%/37.9%) and overweight (17.0%/21.8%) 
Meta-analysis 
 Redcay and Courchesne (27) United States 2.4–46 Brain size at birth is 13% smaller and at 12 mo is 10% greater; stabilized reaching normal range by adulthood 
Study Population Group Sample size, nAge,2Finding (ASD vs. control) 
Retrospective chart review 
 Egan et al. (14) United States Children with ASDs 169 3.89 ± 0.1 Prevalence of overweight: 17.2% vs. 12.5% 
Children with Asperger disorder/PDD-NOS 104 Prevalence of obesity: 21.9% vs. 10.6% 
 Grandgeorge et al. (30) France Children with ASDs 422 Birth HC, body length, and weight: similar at birth 
Typically developing children 153 Macrocephaly* 
 Fukumoto et al. (26) Japan Infants with autism 85 Birth–1 HC (boys): similar at birth, 6 mo,* and 12 mo** 
Control children 14,115 
 Mraz et al. (25) United States Children with ASDs 35 Birth–2 HC: birth–2 wk** and 10–14 mo* 
Healthy infants 37 Height/weight: 1–2 mo* 
National normative data (CDC) 
 Hazlett et al. (23) United States Children with autism 113 Birth–3 HC: normal at birth and 12 mo* 
Control children 189 
 Courchesne et al. (21) United States Children with ASDs 48 Birth–1 HC: birth** and 6–14 mo* (increased from 25th to 84th percentile) 
Cross-sectional study 
 Curtin et al. (12) United States Children from NSCH (2003–2004) 85,272 3–17 Prevalence of obesity: 30.4% vs. 23.6%. 
 Bicer and Alsaffar (2) Turkey Children with autism 164 4–18 Prevalence of overweight/obesity: 58.5% 
 Suren et al. (20) Norway Children with ASDs 376 Birth–3 HC, weight, and length: similar at birth 
Population cohort 106,082 By 12 mo of age: boys, HC similar but greater variability, longer (by 1.1 cm), and heavier (by 300 g); girls, reduced HC (by 0.5 cm), similar length, and lighter (by 150–350 g) 
 Hyman et al. (17) United States Children with ASDs 36732–11 2- to 5-y olds: overweight/obese* 
Controls (NHANES, 2007–2008) 559 6- to 11-y olds: underweight* 
 Xiong et al. (15) China Children with autism 429 2–11 2- to 5-y olds vs. 6- to 11-y olds: at risk of obesity (31.8%/37.9%) and overweight (17.0%/21.8%) 
Meta-analysis 
 Redcay and Courchesne (27) United States 2.4–46 Brain size at birth is 13% smaller and at 12 mo is 10% greater; stabilized reaching normal range by adulthood 

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Studies consistently show that children with ASDs have a sex-specific atypical head growth pattern; at birth, they appear to have a normal or even slightly decreased HC, followed by an increase in the rate of growth of HC up until 12 mo of age before the typical age of clinical identification (19–28). Thereafter, relative to growth during the first year, there is a rapid deceleration in HC between 12 and 24 mo of age, such that it is normal compared with the controls (24, 27, 29). HC is relatively increased in comparison with height (24, 26, 30, 31); however, others report that HC is normal (25) or smaller (32) in relation to height. Many (19, 21, 28, 33) have proposed that this atypical and abnormally accelerated growth in HC is due to dysregulation of growth in general rather than of neuronal growth in the brain, and this may serve as an early indicator of vulnerability to autism in children and among their infant siblings with and without a history of autistic regression.

Biochemical assessment

Analysis of biological specimens for nutrients and nutrient-related substances is imperative in the diagnosis of disease(s) before clinical symptoms are apparent. The knowledge obtained from such analysis helps evaluate treatment plans and monitor effectiveness. Findings on vitamins, minerals, amino acids, FAs, and metabolic markers of children with autism are highlighted in Tables 2–4.

TABLE 2

Studies summarizing vitamin and mineral concentrations in individuals with ASDs1

Case-controlled study Population Group Sample size, nAge, y Finding (ASD vs. control) 
Vitamins 
 Adams et al. (34) United States Autism 55 5–16 Vitamin C (plasma)* 
Neurotypical 44 Biotin (WB), pantothenic acid (WB), vitamin E (serum), and total carotenoids (plasma)** 
Vitamin B-6 (RBC)*: (ASDs = 3 SD of controls) 
Lipoic acid (plasma) and free choline (RBC): similar 
Total choline (RBC), FIGLU (urine), and N-methylnicotinamide (urine)* 
 Ali et al. (35) Oman Children with ASDs 40 3–5 Folate (serum)** 
Controls 40 Vitamin B-12 (serum)** 
 Al-Gadani et al. (39) Saudi Arabia Autistic children 30 3–15 Vitamin E (plasma)** and vitamin c (plasma): similar 
Healthy children 30 
 Adams et al. (40) United States Autism 35 3–9 Vitamin B-6 (plasma): 77% of ASD group had >2 SD above the median value of controls 
Controls 11 
Minerals 
 Russo et al. (46) United States Individuals with autism 102 Birth to >60 Copper (plasma)*: 108.9 μg/dL vs. 86.5 μg/dL 
Neurotypical controls 18 Copper/zinc (plasma)*: 1.41 vs. 1.19 
Zinc (plasma): 80.5 μg/dL vs. 84.7 μg/dL 
 Adams et al. (34) United States Children with ASDs 55 5–16 Lithium (WB), calcium (RBC), and magnesium (serum, WB)** 
Neurotypical controls 44 Copper (WB, RBC), iron, potassium, phosphorus, and boron (RBC)* 
 Lakshmi Priya and Geetha (45) India Children with ASDs 45 4–12 Zinc (hair, nails): variable 
Healthy children 50 Copper, mercury, and lead (hair, nails)* 
Magnesium and selenium (hair, nails)** 
 Adams et al. (44) United States Children with ASDs 51 3–15 Iodine (hair)**: by 45% 
Mothers of children with ASDs 29 Lithium (hair)**: in those aged 3–6 y, and their mothers 
Neurotypical children 40 Potassium (hair)**: in those with low muscle tone 
Mothers of neurotypical children 25 Zinc (hair)*: in those with low muscle tone 
Chromium (hair)**: by 38% in those with pica 
Case-controlled study Population Group Sample size, nAge, y Finding (ASD vs. control) 
Vitamins 
 Adams et al. (34) United States Autism 55 5–16 Vitamin C (plasma)* 
Neurotypical 44 Biotin (WB), pantothenic acid (WB), vitamin E (serum), and total carotenoids (plasma)** 
Vitamin B-6 (RBC)*: (ASDs = 3 SD of controls) 
Lipoic acid (plasma) and free choline (RBC): similar 
Total choline (RBC), FIGLU (urine), and N-methylnicotinamide (urine)* 
 Ali et al. (35) Oman Children with ASDs 40 3–5 Folate (serum)** 
Controls 40 Vitamin B-12 (serum)** 
 Al-Gadani et al. (39) Saudi Arabia Autistic children 30 3–15 Vitamin E (plasma)** and vitamin c (plasma): similar 
Healthy children 

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