Nutritional and Metabolic Status of Children With Autism vs. Neurotypical Children, and the Association With Autism Severity

James B Adams; Tapan Audhya; Sharon McDonough-Means; Robert A Rubin; David Quig; Elizabeth Geis; Eva Gehn; Melissa Loresto; Jessica Mitchell; Sharon Atwood; Suzanne Barnhouse; Wondra Lee

Disclosures

Nutr Metab. 2011;8(41) 

In This Article

Background and Significance

Vitamins, minerals, and essential amino acids are, by definition, essential for human health, primarily due to their critical function as enzymatic cofactors for numerous reactions in the body, such as the production of neurotransmitters and fatty acid metabolism Historically attention has focused on inadequate intake of vitamins and minerals due to poor diet as a major contributing factor to many child health problems in the US and around the world, including anemia (low iron), hypothyroid (low iodine), scurvy (vitamin C deficiency), and rickets (calcium and/or vitamin D deficiency). However, nutritional status depends not only on intake, but also on digestion, absorption, metabolic processing, and metabolic demand. More recently the focus has shifted to the relationship between relative metabolic disturbances and developmental disorders, for example those associated with Attention Deficit Disorder,[1–5] learning disorders,[6] and intellectual development.[7] We hypothesize that nutritional insufficiency and metabolic imbalances may play a role in autism spectrum disorders (ASD).

There have been several studies of the nutritional and metabolic status of children with autism, but each focused on study of only a few biomarkers. Three studies have demonstrated that children with autism have impaired methylation, decreased glutathione, and oxidative stress,[8–10] and those studies demonstrated that nutritional supplementation (with vitamin methyl-B12, folinic acid, and trimethylglycine) is beneficial. One study in Romania found normal levels of vitamin B12 and folate in children with autism compared to controls, but low levels of plasma glutathione.[11] Several other studies have also demonstrated increased oxidative stress.[12–15] One study[16] found that children with autism had high levels of plasma vitamin B6 pre-supplementation, and this finding was confirmed in a follow-up study,[17] suggesting a metabolic imbalance in B6. One study of dietary intake of 111 autistic children in China found that most had inadequate intake of folic acid, vitamin B6, vitamin A, Vitamin C, and zinc.[18] One study of vitamin D status in Egypt found that young children with autism had lower levels of vitamin D, both 25(OH)D and 1,25(OH)(2)D compared to age-matched controls.[19] One study in Slovakia found that children with autism had significantly higher levels of vitamin C and beta-carotene, but normal levels of vitamin A and vitamin E, compared to older teen controls.[20]

There are several studies of minerals in children with autism. One study found that young US children with autism and their mothers had unusually low levels of lithium compared to neurotypical children and their mothers; lithium is receiving increasing recognition as being an essential mineral.[21] Two large studies of iron status found that US and Canadian children with autism had anemia in 8% and 16% of cases, respectively.[22,23] One small study of minerals in red blood cells found that young Canadian children with autism (n = 20) had lower levels of RBC selenium and RBC molybdenum than neurotypical children (n = 15) of the same age,[24] but similar levels of most other minerals. A small study of zinc and copper in plasma found that British children with autism (n = 20) had similar levels to neurotypical children (n = 30).[25] In contrast, a study of Turkish children with autism (n = 45) found that they had lower levels of zinc in plasma and RBC compared to neurotypical children (n = 41).[26] One study[27] reported low levels of plasma zinc and high levels of serum copper in young children with autism as compared to published reference ranges, but.the lack of in-study controls is a weakness of this study.

There have been several studies of essential amino acids in autism with conflicting results. Increased levels were found by Aldred et al 2003,[28] both increased and decreased levels by Moreno et al 1996,[29] and decreased levels by Rolf et al 1993[30] and Arnold et al 2003;[31] the latter found only decreased methionine in the autism group on a standard diet. One limitation of the studies was their small population size (less than 25 participants in each arm). Another very important limitation is that fasting status was unclear in two of the studies[29,30] or only involved a limited (2–4 hours) fast in another study.[31] Only one of the studies[28] involved overnight fasting; this is important as amino acid values are not comparable unless all are done in a fasting state. One of the studies[28] involved very different age ranges for the controls and the autistic group which is important as pediatric reference ranges for some plasma amino acids vary substantially with age.[32] Thus, larger, more rigorous studies are needed.

The purpose of this study is to investigate the nutritional and metabolic status of children with autism compared to neurotypical children of similar age and gender, and to determine if some nutritional and metabolic biomarkers may be associated with the severity of autism. This study includes a broad array of biomarkers because that helps provide a more complete understanding of nutritional status, including vitamins, minerals, amino acids, and other metabolic biomarkers. The children with autism who participated in this study then continued into a randomized, double-blind, placebo-controlled study of the effect of a vitamin/mineral supplement, and the details of that follow-on investigation are reported in two companion papers [Adams et al, Effect of a Vitamin/Mineral Supplement on Children with Autism: Part A Nutritional and Metabolic Results, submitted, and Adams et al, Effect of a Vitamin/Mineral Supplement on Children with Autism: Part B. Effect on Symptoms.]. A strength of this study is the use of neurotypical controls of similar age, gender and geographic distribution, tested concurrently under identical conditions to the autism group, with blinded evaluation of samples by the laboratories.

Methodology

This paper reports on the baseline levels of children with autism compared to neurotypical children. Neither group of children had taken any vitamin/mineral supplements in the two months prior to the study. This study was conducted with the approval of the Human Subjects Institutional Review Board of Arizona State University, study protocol number 0801002499.

Participants Participants were recruited during May to December 2008 from Arizona with the help of the Autism Society of Greater Phoenix and the Arizona Division of Developmental Disabilities. All parents and children, where appropriate for age and developmental ability, signed parent consent/child assent forms.

Enrollment Criteria

1) age 5–16 years old;

2) no usage of a vitamin/mineral supplement in the last 2 months

3) no current use of any chelation treatment

4) Autism Group: prior diagnosis of autism, PDD/NOS, or Asperger's by a psychiatrist or similar professional, with written verification (no additional assessment was done in this study)

5) Control Group: in good mental and physical health, and no siblings with autism spectrum disorders, and no evidence of Attention Deficit Disorder by parent report (no additional assessment was done in this study)

Participants

The characteristics of the study participants are listed in Table 1, and their physical and behavioral symptoms (per the ATEC) are listed in Table 2.

Study Protocol

1) Participant parents contacted the study coordinator, and the study was explained by telephone. Consent/assent forms were sent to the parents for review, and then signed copies were brought to the study coordinator. The Principal Investigator (J.B. Adams) also discussed the study personally with each participant.

2) Parents of children with autism completed three questionnaires relating to the severity and symptoms of autism (see below).

3) The study physician conducted a physical exam to determine that the children were in adequate health for participating in the study.

4) Morning blood samples (50 ml) were collected after an overnight fast (8–12 hours). Morning urine samples were collected, and in almost all cases these were first-morning (overnight) urines.

5) All study data (questionnaires and laboratory samples) were assigned a coordinating subject code. All laboratory analyses were done blinded to subject group (Autism or Control).

Lab Measurements Minerals and plasma amino acids were measured by Doctor's Data (St. Charles, IL, USA - http://www.doctorsdata.com). Vitamins, serum ferritin, and all other biomarkers were measured by Vitamin Diagnostics (South Amboy, NJ, USA; http://www.europeanlaboratory.nl). Both laboratories are certified by CLIA, the Clinical Laboratory Improvement Amendments program operated by the US Department of Health and Human Services which oversees approximately 200,000 laboratories in the US.

Measurement methods are summarized in Table 3. For urine analyses, correction for variations in dilution was done by adjusting for specific gravity[33] or by normalizing to grams of creatinine.

Vitamins were measured in the blood compartment (serum, plasma, or RBC) where they are most highly concentrated, or if evenly distributed intra- and extra-cellularly then whole blood was measured. Fat-soluble vitamins (A, D, E, K) are primarily concentrated in serum. For water-soluble vitamins, some are primarily in the plasma (like vitamin C), whereas others (like pantothenic acid) are significantly present in both serum and RBC, so whole blood was used. This approach then provides the best estimation of total body levels. Whole blood measurements are not commonly used for laboratory assessments because of challenges in processing the samples. However, by the use of vitamin-specific microbiological organisms as done in this study, whole blood levels are measured with a high degree of reliability.

Essential minerals were measured in RBC, serum, whole blood, and (for iodine) in urine. In most cases, serum reflects an average of the last several days, RBC reflects an average of the last several months, and whole blood is an average of both. Serum Na, K, Mg, Ca, P, Fe were analyzed on an automated clinical chemistry analyzer (Olympus AU680, Olympus America Inc.; Centerville, Pa., USA) using commercial assays. Essential minerals were measured in RBC in all cases except for sodium, lithium, and iodine; most were also measured in whole blood and/or serum depending upon which compartment is known to have the higher concentration for that mineral. Lithium was only measured in whole blood because it is more detectable there. Iodine was measured in urine (see below) because it is more detectable and reliably measured in urine than in blood. Whole blood and packed red blood cells were collected in a potassium EDTA trace metal free (royal blue top; BD Vacutainer, Franklin Lakes, NJ). Packed red blood cells were spun for 15 minutes in a centrifuge at 1500 g (g-force), the plasma and buffy coat were removed and the remaining packed red blood cells were submitted for testing. Elemental analysis was performed after digesting an aliquot of sample using a temperature controlled microwave digestion system1 (Mars5; CEM Corp; Matthews, SC), following the same procedure for nitric acid microwave digestion and sample procedure as used previously for hair.[34] The digested sample was analyzed by Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) (Elan DRCII; Perkin Elmer Corp; Shelton, CT). Results were verified for precision and accuracy using controls from Doctor's Data and Seronorm whole blood controls (Sero; Billingstad, Norway).

Urine iodine was analyzed by ICP-MS using a modification of the methods reported in the Analytical section of the report by the Agency for Toxic Substances and Disease Registry (ATSDR 2004). Urine results are expressed per gram creatinine.

Amino Acids After an overnight fast blood samples were collected into purple top (EDTA) tubes. Blood was centrifuged within 30 minutes, and plasma was mixed with 5-sulfosalicylic acid to precipitate proteins prior to freezing for 24 hours prior to shipping. Plasma amino acids were analyzed by a reversed phase high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS) technique (Prostar 420 HPLC autosampler, Prostar 210 solvent delivery module, 1200 L mass spectrophotometer, Varian, Inc.; Palo Alto, CA) using a method developed at Doctor's Data. Results were verified for precision and accuracy using in-house controls and a Native (Physiological) Sample Standard (Pickering Laboratories). Note that the measurement process results in oxidation of any cysteine, so that the measurement of "cysteine + cystine" is actually a measure of the combination of cysteine and cystine. The same is true of homocysteine and homocystine.

Assessing Autistic Symptoms and Severity

Three tools were used to assess the severity and symptoms of autism, namely the Pervasive Development Disorder Behavior Inventory (PDD-BI),[35] Autism Evaluation Treatment Checklist (ATEC)[36] and Severity of Autism Scale (SAS).[37] For the PDD-BI, a modified Autism Composite was used, following the example of a previous study.[37] That is, the Semantic/Pragmatic Problems (SemPP) subscale was omitted as children with no spoken language inappropriately score as less severe than those with limited language. The resulting modified Autism Composite consisted of Sensory/Perceptual Approach, Ritualisms/Resistance to Change, Social Pragmatic Problems, Social Approach Behaviors, Phonological and Semantic Pragmatic subscales.

Statistical Analysis

Several types of statistical analyses were used, depending on the research question being addressed. In comparing levels between groups (such as children with autism vs. neurotypical children), 2-sided unpaired t-tests were used. The unpaired t-tests were either done assuming equal variance or unequal variance, based on the results of a test for equal variance. For individual comparisons a p value of 0.05 or lower was assumed significant. However, in order to maintain an overall significance of 5% when multiple comparisons were considered, a smaller per-test p-value was considered significant based on a Bonferroni analysis, and this p-value is specified at the beginning of each of the result sections. For example, if making 5 comparisons, then an overall significance of 5% is achieved if the p-value is set at 0.05/(5 comparisons) = 0.01. We use the term "marginally significant" if the p value is less than 0.1/(number of comparisons). We use the term "possibly significant" if the p-value is less than 0.05 but not low enough to be marginally significant; this means that the result would be significant if only one comparison were made, but could be a statistical fluke due to the making of many comparisons, so further studies are needed to confirm or invalidate the result.

Some of the data for essential minerals were not normally distributed, so in those cases a non-parametric Wilcoxon test was used instead of a t-test. Pearson correlation coefficients were obtained to determine the strengths of linear relationships among the variables involved in the analyses.

Note that for a few measurements there was some data below the detection limit. In those cases the value of the detection limit was substituted for the data point; so, for cases where some samples were below detection limit, our reported measured values are an upper bound to the true value.

Correlation and regression analysis was employed to examine the relationship between the severity of autism (assessed by the ATEC, PDD-BI, and SAS) and the biomarkers of nutritional and metabolic status. For the selected dependent and independent variables, step-wise linear regression analyses were conducted: initially all independent variables were included in the regression; then at each step, the variable with the highest p-value was eliminated, and this process was continued until the adjusted R2 value began declining. Thus, the goal was to determine the best fit to the sample data for the selected model, taking into account the correlation among the independent variables. Since the data had several missing values (due to missing lab or behavioral data), the regression analyses were conducted by restricting the analysis to "complete cases" only (i.e., where there were no missing values for any of the variables in the initial analysis step). Due to the large number of biomarkers compared to the number of participants, the regression analyses were first conducted by category; for example, vitamins vs. the PDD-BI as the dependent variable. After determining, for each category, the few within-category biomarkers that had the greatest association with autism severity, an "overall" step-wise regression was performed on those biomarkers with the greatest association with autism severity. Since that "overall" analysis involved a large number of variables compared to the number of participants, the overall analysis needs to be interpreted cautiously.

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