Behavioral Effects of Micronutrient Supplementation on Children with ADHD
Francis M. Crinella, Ph.D.
Sabrina E.B. Schuck, Ph.D.
Natasha Schneider, Ph.D.
Andrew Schneider, D.O.
Melody Yi, Ph.D.
Angela Liang, M.A.
Child Development Center
Department of Pediatrics
University of California, Irvine
1
A. SPECIFIC AIMS
The most common treatment for children with ADHD is stimulant drugs, which have proven to be
efficacious in a majority of carefully diagnosed cases. However, stimulants have been found to be
ineffective in a small proportion of children with apparent ADHD, and, irrespective of stimulant efficacy in
reducing ADHD symptoms, a number of children will experience adverse side effects with their use.
Further, many families are unalterably opposed to the use of prescription drugs to treat their child with
ADHD, preferring instead to use complementary and alternative methods (CAMs), such as cognitive
behavior therapy, neurofeedback, aerobic exercise, and dietary restrictions and/or supplementation. In the
present study, we propose to investigate the effect of one CAM, dietary supplementation, using a
combination of vitamins, minerals and essential fatty acids (micronutrients), to determine the effects of the
supplement on the behavior of children with ADHD. A secondary aim will be to assess the strength of an
often-reported CAM placebo effect, by employing an experimental design in which the outcomes of the
placebo condition will be contrasted with those in the a) active treatment group, but also b) a group
receiving neither placebo nor active treatment.
Specifically, we intend to use standard diagnostic criteria to identify a group of 120 children, 7 to 12 years
of age, who meet DSM-IV criteria for ADHD, either the predominantly hyperactive/impulsive or the
combined subtype (i.e., DSM-IV 314.01). All children will participate in a standardized format, 12-week
social skills training program (SSTP), and we will additionally randomly assign each of the subjects to one
of three treatment groups:
1. SSTP + Micronutrient supplementation group (n = 40)
2. SSTP + Placebo supplementation group (n = 40)
3. SSTP only group control (n = 40)
In Phase I of the study, children will be screened, randomized to one of the above treatment groups, and
then followed over an eight week ramp-up period, during which Group I will begin taking the active
micronutrient supplement, Group II will begin taking placebo, and Group III will simply be waiting to begin
their SSTP. In Phase II, the children in all three groups will be assigned to a 12 week, bi-weekly SSTP,
while continuing on with their respective supplementation assignments. In Phase III, following the
conclusion of the 12 week SSTP, children in all three groups will be offered the opportunity to continue
with the active supplement for an additional 12 weeks.
At the onset of Phase I, we will evaluate each child with respect to diagnostic indices of ADHD and
inclusion/exclusion criteria. Included in this evaluation will be results of the SWAN rating scale and the
Social Skills Intervention System rating scales (SSIS-RS), as well as standardized laboratory
neuropsychological assessments. Prior to beginning the SSTP, the child will once again be rated on the
SWAN and the SSIS-RS. During Phase II, children will be evaluated weekly over the 12 week intervention
period by both parents and trainers, using the SSIS-RS. We will also conduct an exit evaluation at the
conclusion of Phase II, using the SWAN, SSIS-RS and laboratory neuropsychological measures. At the
conclusion of Phase III, we will perform and exit evaluation using the SWAN, SSIS-RS, and laboratory
neuropsychological measures. Over the course of the study, then, behavior ratings will be performed at
16 discrete time points, and laboratory neuropsychological tests will be conducted at 3 discrete time
points.
2
Data will be analyzed using latent curve and structural equation modeling techniques, which is considered
the optimal strategy for longitudinal studies of growth, given children of varying ages, given multivariate
measures over several time points.
B. BACKGROUND AND SIGNIFICANCE
ADHD is a relatively common disorder of childhood onset, characterized by a persistent pattern of
overactivity, inattention and impulsivity. The worldwide prevalence of ADHD has been estimated at about
5% [1], although differences in diagnostic criteria, methods of assessment and sample selection have
resulted in estimates ranging from 2% to 11% [2-4]. While the pathophysiology of ADHD remains unclear,
variations in the brain‘s dopaminergic networks have long been implicated, primarily based on the
effectiveness of stimulants (dopamine agonists) such as dextroamphetamine or methylphenidate and their
various refinements in reducing ADHD symptoms [5-6]. Indeed, stimulants have proven efficacious for a
vast majority of children in whom the diagnosis of ADHD has been carefully confirmed [7, 8]. However,
stimulant medications have not been uniformly effective in modifying ADHD symptoms, with estimates of
non-responders ranging from 25% to 35% [9]. Additionally, stimulant drugs, irrespective of their efficacy in
reducing ADHD symptoms, are not without adverse side effects [e.g., 10]. Finally, even if stimulants were
found to be 100% efficacious with no adverse side effects, many families would remain unalterably
opposed to treatment of their child with prescription drugs, preferring instead to seek out complementary
and alternative methods (CAMs) [11, 12].
CAMs include interventions such as cognitive behavioral therapy, biofeedback, aerobic exercise,
elimination diets, and dietary supplementation. Recent surveys have shown that CAMs have become
quite popular, and are often the initial form of treatment for ADHD. For example, in one sample of 290
children diagnosed with ADHD, 60% had been given some form of dietary therapy [13]. In a similarly large
sample, 67% of families had tried some form of CAM, the most common being modified diet (31%),
micronutrients (15%) or other dietary supplements (e.g., herbal remedies; 11%)[12].
1. Nutrition and ADHD
A relationship between diet and health was prominently mentioned in the writings of both Hippocrates
(460-377 B.C.) and Galen (131-201 A.D.), but it may have been Benjamin Rush, in his 1786 address to
the American Philosophical Society, who first acknowledged the role of diet in the treatment of “moral”
(i.e., mental) illnesses [14]. Noting that “both the quality and quantity of aliment has an influence upon
morals,” Rush went on to recommend a milk and vegetable diet because of the “efficacy of a vegetable
diet upon the passions.” The Italian physician and criminologist, Cesare Lombroso, in his L’uomo
delinguente (1888), also spoke of malnutrition, notably pellagra, as one of causative factors in aberrant
behavior.
Two centuries later, the first link between diet and symptoms specific to ADHD appeared in Feingold’s
Why your child is hyperactive, in which the author proposed the elimination of salicylates, as well as
artificial food colorings and preservatives to ameliorate the symptoms of ADHD [15]. Theories concerning
diet and ADHD have abounded since then, and these can be roughly categorized into a) those
emphasizing the benefits of elimination diets, and b) those emphasizing the advantage of nutritional
supplementation. The present proposal is focused only on the benefits of nutritional supplementation.
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Nutritional Supplementation as Therapy
In modern times, a nodal point was achieved when Linus Pauling coined the term “orthomolecular
psychiatry” in his 1968 Science paper [16]. Pauling defined orthomolecular psychiatry as: “the treatment
of mental disease by the provision of the optimum molecular environment for the mind, especially the
optimum concentrations of substances normally present in the human body.” In the ensuing four decades,
treatment employing megadoses of vitamins and/or minerals has been largely discredited [17,18];
However, a number of peer-reviewed studies have been published showing that forms of nutritional
supplementation other than strictly orthomolecular approaches may be effective in treating problems as
diverse as schizophrenia [19], manic states [20], depression [21, 22], hyperaggression [23,24], dyspraxia
[25], and ADHD [26-39; see ref. 40 for recent comprehensive review].
Rationale for Nutritional Supplementation
Micronutrient deficiencies have long been associated with adverse occurrences at several systemic
levels, including a) epigenetic (i.e., affecting gene expression), b) neurophysiological, and c) behavioral
[41]. Any of these may ultimately affect behavior regulation. By way of example, drowsiness and lethargy
in breastfed infants has been related to maternal nutritional insufficiency [42]. In turn, the drowsiness and
lethargy impede normal orienting and exploratory processes [43]. These weak and inadequate
investigatory responses, and a tendency to be drawn to irrelevant stimuli, is considered by a number of
investigators be the overarching neurocognitive deficit in ADHD, i.e., disinhibition [44].
Viewed holistically, insufficient intake of micronutrients will lead to poorer overall health [45, 46]; in turn,
poorer overall health has been related to cognitive and/or behavioral deficits in the child [43]. For
example, inadequate iron (Fe) and zinc (Zn) status are two of the most common nutritional deficiencies in
children, and these same two micronutrients have been clearly identified as essential for cognitive
development in the child [49-57], playing essential roles in the development of the a) neuronal
architecture and b) the neurochemical matrix of the brain [56,58,59]. Additionally, Fe and Zn are
preeminent among the micronutrients for their capacity to compete for uptake with toxic metals such as
methylmercury or manganese [Mn]), thereby conferring protection again the toxic effects of these metals
on neuronal systems [60-70].
Nutritional supplementation has been defined as “the correction of innate or acquired chemical imbalances
using amino acids, vitamins, minerals and other biochemicals that are naturally present in the body” [71].
Evidence that such imbalances may be present in U.S. children may be found in sources such as the
1997 investigation by Munoz et al., who reported, after surveying a population of 3307 children, that only
1% met all the U.S. Department of Agriculture’s (USDA) recommended daily allowances for nutrient
intake [72]. Alarmingly, 30% failed to meet the minimum RDA for fruits, grain, meat and dairy products,
and 36% failed to meet the minimum RDA for vegetable intake. In a similar survey, Suitor and Gleason,
using a national sample of 2,692 children and adolescents, found that the children failed to meet
estimated average requirements (EARS) for a wide range of nutrients, including Vitamins A, C, E, and B 6
as well as folate, magnesium and calcium [73]. In 2006, Guenther et al, using 24-hour recall from each of
8,070 respondents in a National Health and Nutrition Examination Survey, reported that the proportions of
sex-age groups meeting the USDA recommendations for fruits and vegetables ranged from 0.7% (for
boys aged 14 to 18) up to 48% (for children aged 2 to 3 years) [74]. Thus, it is reasonable to assume that
4
a child with ADHD is likely to have a nutritional deficiency because a substantial percentage of all children
in the U.S. population appear to have such deficiencies [75].
Caveat
Before proceeding further, it would be well to put aside the notion that the authors believe that ADHD, in a
significant number of cases, is “caused” by nutritional deficiencies. At this time, nutritional factors are
dwarfed by a more impressive body of evidence showing that ADHD is a highly heritable condition, with
most studies of family lineage resulting in heritability estimates in the vicinity of .80 [76,77], a
determination that now derives support from convergent molecular biological investigations [78, 79].
There are also well-documented non-genetic risks for ADHD, such as maternal gestational diabetes [80],
multiple births and/or low birth weight [81], prematurity [82], hypoxic ischemic injury [83], prolonged
maternal gestational stress [84], and most recently, in vitro fertilization (IVF) [85-88; see 89 for a recent
review of known risk factors]. In addition to adverse prenatal and perinatal experiences, geneenvironment interactions also need to be considered [see ref. 90 for an especially illuminating review].
However, not all children with gene polymophisms linked by association to ADHD will be diagnosed with
the disorder, nor will all children who have histories that include other known risk factors for ADHD
manifest the disorder; conversely, many children with ADHD will have histories devoid of any known risk
factor.
It is possible, then, that nutritional deficiencies could, in rare instances, be sufficient in themselves to
precipitate full-blown (i.e., diagnosable) ADHD; however, it is much more probable that dietary
deficiencies could act to lower thresholds for the expression of ADHD symptoms. This could take place by
producing a suboptimal matrix of nutrient support for neural networks that mediate attentional processes,
perhaps promoting an escalation of subclinical symptoms to full-blown manifestations of the disorder. The
foregoing argument might be especially for children with genetic and/or other known risk factors. Indeed,
there is some evidence to suggest that a lack of essential nutrients can lower thresholds for appearance
of ADHD-like symptoms in at risk individuals. Examples of a few possible mechanisms might include: (1)
The cognitive benefit that accrues with breast feeding in infancy is apparently linked to single nucleotide
polymorphism for FADS2, which promotes synthesis of long chain polyunsaturated fatty acids (LCPUFAs) from their precursors [91]. FADS1/2 genetic variations may also condition the feedback regulation
of PUFAs and alter gene expression in LC-PUFA pathways, thereby affecting synaptic plasticity [92]; (2)
the creation of persistent imbalances and/or acute depletions of enzymatic co-factors essential for the
synthesis and degradation of neurotransmitters such as dopamine (DA) or serotonin (5HT)
[57,58,115,121]; (3) the depletion of nutritionally-derived co-factors for antioxidants, such as Zn or copper
(Cu), thereby oxidatively stressing neuronal cell membranes [65,94,124]; (4) disruption of the balance
among essential minerals, allowing some to escalate to neurotoxic levels [62,64,67 69,94]; and, (5)
depletion of nutrients essential for mitigating adverse reactions to impoverished environments [54,56,96].
Known Brain-Micronutrient Interactions
One approach to classifying the dietary deficiencies that could conceivably play a part in the manifestation
of ADHD symptoms would be to identify single micronutrients, or discrete interrelated groups thereof, that
have been shown to be associated with development of the neuronal architecture and/or regulation of
brain chemistry, especially with reference to neural systems that mediate attentional processes. The list of
5
candidate nutrients is virtually endless, but a number of the better-known studies illustrating such
relationship(s) are shown in Table 1.
6
Table 1. Studies illustrating relationships between components of the daily diet and brain functions
MICRONUTRIENT
Vitamin A
Vitamin B1 (thiamine)
Vitamin B6 (pyridoxine)
Vitamin B9 (folic acid)
Vitamin B12
Vitamin E
Choline
Calcium
Manganese
Iron
Zinc
Chromium
Magnesium
Docosahexaenoic acid [DHA]
DEMONSTRATED RELATONSHIP TO BRAIN FUNCTION
Essential for the retinoid receptors in cortex, hippocampus, and dopamineinnervated areas such as striatum, nucleus accumbens, and olfactory
tubercle
Essential for hippocampal long-term potentiation
Synthesis of acetylcholine, GABA, and glutamate
Synthesis of dopamine, serotonin, norepinephrine, epinephrine, histamine
and GABA
Cofactor for enzymes necessary for conversion of tryptophan to serotonin
Cofactor for enzymes necessary for conversion of tyrosine to
norepinephrine
Synthesis of monoamines essential for myelination
Protection of neuronal membranes from free radical damage
Promotes structural integrity of cell membranes thereby enhancing neuronal
signaling
Precursor to acetylcholine
Rapid increase in acetylcholine synthesis via increased size of synapses
Increase in brain phosphatides leading to improved memory function
Cellular release of neurotransmitters, and chemical signaling between cells
Cofactor for enzymes such as manganese superoxide dismutase, arginase,
and pyruvate carboxylase
Antioxidant activity mitigates effects of superoxide and hydrogen peroxide,
protecting brain areas critical for attention, such nigrostriatal dopaminergic
system, from lipid peroxidation
Essential cofactor for ATP energy in brain
Essential role in production of hemoglobin, ensuring sufficient oxygen
transport to brain
Cofactor in metabolism of tyrosine, precursor to dopamine
Influences dopamine receptor density in midbrain
Increases binding of dopamine and serotonin to proteins in frontal cortex
Necessary for myelination
Necessary for synthesis of proteins that regulate gene expression
Cofactor for over 100 enzymes, including one form of superoxide dismutase
that protects brain from toxic free radical species
Necessary for fatty acid absorption, indirectly affecting catecholamine
synthesis
Necessary for production of melatonin, which helps regulate dopamine
function
Supports neuronal functioning in zinc-enriched brain regions such as
hippocampus
Increases insulin sensitivity, promoting enhanced central noradrenergic and
serotonergic activity
Inhibits activity at calcium channels, producing neural membrane stability
Inhibits calcium-mediated neurotransmitter release
Alters configuration of post-synaptic receptors
Enhances nerve growth factor in development, promoting neurite growth in
hippocampus
Neuroprotective against oxidative stress
Important determinant of astrocyte maturation, vascular coupling, and
cortical glucose uptake and metabolism
Associated with corticolimbic gray matter volumes
Increased prefrontal cortex activation during a sustained attention task in
healthy boys
7
REFERENCES
[97-99]
[100,101]
[102,103]
[104-107]
[106]
[108]
[96,109,110]
[111]
[112-116]
[87-90]
[121-124]
[125-127]
[128,129]
[130-146]
Eicosapentaenoic acid [EPA]
Arachidonic acid [AA]
Amino acids (tryptophan,
tyrosine, phenylalanine)
Oligomeric proanthocyanidins
(grape seed extract)
Supports dopaminergic mesocorticolimbic pathways
DHA precursor
Involved in neural, enzymatic and anti-inflammatory functions.
Vital for basic formation of cell membranes; maintains optimal state of
neural membranes enabling membrane fluidity and thickness, which affects
cell signaling
Precursors to catecholamines and serotonin
Protects brain cells from lipid peroxidation caused by free radicals
[147]
[148-152]
[153-157]
[158-159]
Note: The foregoing table is far from exhaustive, but is sufficiently representative of the multiple avenues
through which micronutrients might affect neural processes.
Variants among Nutritional Interventions for ADHD
Studies of nutritional supplementation for the treatment of ADHD can be classified in a number of ways. A
convenient scheme might be: 1) single nutrient, i.e., supplementation with a specific nutrient to address a
specific neurochemical deficiency thought to be present in ADHD; 2) nutrient combinations, or “cocktails,”
said to address more complex nutritional deficiencies posited to accompany ADHD; and, 3) broad
spectrum treatments, using vitamins, minerals, and (often) essential fatty acids, in attempting to address
suspected, but unknown nutrient deficiencies in ADHD.
Rucklidge et al.’s recent survey of nutritional supplementation in ADHD took into account 27 published
studies, contrasting the effects of single ingredient prescriptions, such as vitamins, minerals, amino acids,
essential fatty acids, and botanicals, with multi-ingredient approaches [40]. The authors classified each
study according to adequacy of design: case study (CS; n = 1); open label trial (OLT; n = 8); and
randomized placebo controlled trial (RCT; n = 18), as well as their outcomes (effect; no effect; unclear).
Using this approach, the reviewers found that eight of 22 studies of single-nutrients, and five of five
approaches using either “cocktails” or a multi-ingredient product (i.e., vitamins + minerals + fatty acids)
had demonstrated what could be loosely classified as an “effect.” Of the eight single nutrient studies
purportedly showing an effect, seven consisted of RCTs; of the five combination formula studies
purportedly showing an effect, all were OLTs. Hence, of the 27 studies reviewed, only 13 appeared to
have shown a positive effect--but of the 13, only seven met stringent guidelines for adequacy of clinical
trials, i.e., RCT, as shown in Figure 1.
Figure 1. Breakdown of effects of nutritional studies in ADHD (from Rucklidge et al.[40]).
8
ALL PUBLISHED STUDIES OF NUTRITIONAL
INTERVENTIONS IN ADHD (N = 27)
CASE STUDY
(n = 1)
OPEN LABELTRIAL
(n = 8)
RANDOMIZED CONTROL TRIAL
(n = 18)
NO EFFECT OR UNCLEAR (n = 11)
SIGNIFICANT EFFECT (n = 7)
Table 2 briefly describes each of the seven studies determined to have had an effect, while also meeting
RCT criteria. As may also be seen in the table, even among these investigations, which seemed to have
met minimum criteria for experimental adequacy (i.e., RCT), several had less than optimal design
characteristics, including: (1) too few subjects; (2) very young subjects, making the ADHD diagnosis
somewhat problematic; and (3) treatments that took place over very short time frames.
Table 2. Published RTC studies showing positive effect of nutritional supplementation on ADHD
symptoms (adapted from Rucklidge et al.[40]).
INVESTIGATORS
1. Kozielac & StarobratHermelin [160]
2. Bilici et al. [161]
SUPPLEMENT(S) USED
Magnesium (3 mg/lb)
BASIC DESIGN
Ages 7-12; n = 75; 24
wks
Boys, 6-14; n = 400; 12
weeks
3. Akhondzadeh et al.
[162]
Zinc (55 mg; as adjunct to
methylphenidate)
Ages 5-11; n = 44; 6
weeks
4. Van Oudeheusden &
Scholte [33]
Carnitine (100 mg/kg)
Boys 6-13; n = 26; 24
weeks
5. Sinn & Bryan [26];
6. Sinn et al. [27]
Essential fatty acids (EFA): EPA (558
mg); DHA (174 mg), and GLA (60 mg)
with or without additional micronutrients
Ages 7-12; n = 132; 15
wks plus 15 wk
extension, with all
groups receiving EFA
7. Trebaticka et al. [34]
Pycnogenol (1 mg/kg)
RCT; Boys 6-14; n = 61;
8 weeks
Zinc (150 mg)
9
OUTCOME
Decrease in hyperactivity
with magnesium
Zinc superior to placebo
for hyperactivity,
impulsivity and impaired
socialization
Zinc superior to placebo
for parent and teacher
ratings
Improvement on parent
and teacher ratings of
ADHD symptoms
Placebo group higher on
parental measures of
inattention, hyperactivity,
impulsivity, switching and
controlling attention; no
change on teacher ratings
Treatment significantly
decreased hyperactivity as
rated by both teachers and
parents; increased
attention, visual-motor
coordination, and
concentration; Relapse
with washout.
As also seen in Table 2, the seven studies that appeared to have produced positive results were all
studies in which single nutrient or a restricted set of nutrients were used. Further, only one nutrient, zinc
(Zn) was found to be effective in more than one study.
Critique of Single Nutrient Approaches
Currently, there seem to be no end to recommended nutritional interventions, following piecemeal evidence
that: (1) The level of one or another nutrient falls into a range considered outside “normal” reference values
in groups of ADHD individuals; or (2)) there are statistically significant differences in levels of a particular
nutrient when ADHD and non-ADHD controls are contrasted. One of the major problems in either instance is
that conclusions are based on mean differences between an ADHD group and some absolute clinical
standard for nutrient status (e.g., for Zn, 80-120 µg/dL) or values obtained from non-AHDH controls within
the particular experiment. But our experience has shown that within ADHD cohorts, it is often the case that
significant differences in significantly depressed mean levels of a single nutrient represent the influence of
abnormal values obtained from only a few cases, and that the majority within any ADHD group will have
micronutrient that are not appreciably different from reference values, or from the majority of non-ADHD
controls.
For example, in a study completed a number of years ago at our center, we found significant group
differences in head-hair manganese (Mn) levels when ADHD and non-ADHD children were contrasted [163].
The mean value for head hair Mn in the ADHD group was higher than that of controls (0.1192 +/- .1042
ppm vs. .0336 +/- .0336 ppm). However, there was a significant difference in group variances (F = 4.290;
p = .048), reflecting the fact that the higher mean Mn levels for the ADHD group could be attributed
entirely to extreme scores of only a few members of the ADHD group (mean =.2498 +/- .1166). Since
each member of the ADHD cohort had been carefully diagnosed as part of a multi-site NIH funded study
[164], it seemed improbable that, in most cases, symptoms sufficient to warrant an ADHD diagnosis could be
linked to abnormal Mn levels.
In two studies conducted by Walsh and colleagues [71,124], the authors reasoned (plausibly) that, a) since
zinc (Zn) is known to be a modulator of the neurotransmitter gamma aminobutryic acid (GABA) activity in the
hippocampus, and b) copper (Cu) is in dynamic competition with Zn in the body, then c) the Cu/Zn ratio
might be an indicator of abnormal brain functioning. Among the clinical disorders linked to abnormal Cu/Zn
ratios by the authors is ADHD. In their first study [71], Cu/Zn ratios were contrasted between a group of 135
assaultive individuals with and 18 non-assaultive controls. The investigators found significant mean
differences (p <0.01) as indicated in Table 3.
Table 3. Comparison of Cu/Zn levels in assaultive vs. non-assaultive males (from Walsh et al, [41]).
GROUP
Assaultive (n = 135)
Non-assaultive (n = 18)
MEAN Cu/Zn RATIO
1.40
1.02
STANDARD DEVIATION
0.54
0.18
As can be readily determined from examination of the table, there were vastly unequal group variances,
which the authors (correctly) took into account in their statistical contrast. However, since the assaultive
10
individuals quite obviously had an extremely wide range of Cu/Zn ratios, either one or the other of the
following was likely to have been the case: (1) If the shape of the distribution of scores for the assaultive
group were relatively normal (which is unlikely), there would have been a considerable overlapping of scores
for the two groups; or (2) if the distribution of scores for the assaultive group were skewed significantly to the
right (the more likely case), then it may have been of some interest to learn to what extent the apparent
mean differences may have been the result of a subclustering of extreme scores, and that the majority of the
“abnormal” subjects actually fell within the normal reference range for Cu/Zn ratios.
In the second study by Walsh et al. [124], 207 consecutively-presenting patients with ADHD and aggressive
behavior were enrolled in a study in which specific chemical imbalances were identified and subsequently
treated with individualized biochemical therapies. Seven distinct chemical imbalances were identified in this
sample: (1), elevated copper-zinc (Cu/Zn) ratio (75.4%); (2) depressed blood histamine (29.5%); (3) elevated
blood histamine (37.7%); (4) elevated urine kryptopyrroles (32.9%); (5) heavy-metal overload (17.9%); (6)
glucose dyscontrol (30.4%); and (7) malabsorption (15.5%). As an outcome of the treatments provided,
reduced frequency of assaults was reported for 92% of the treatment-compliant patients (P<0.001), with 58%
of this group achieving “elimination of the [assaultive] behavior.”
While these results are encouraging, the study had several weaknesses. First, the study was not a RCT (i.e.,
randomized assignment, double-blind, placebo-controlled study). Indeed, both the parents, who acted as
informants on the behavior rating instruments, and the nurses, who administered the rating instruments,
were aware of the fact that the child was being actively treated. Further, since the total of those subjects
having various abnormal biomarkers was 239.3%, it is obvious that, on average, a single subject would have
been treated for more than two specific nutritional imbalances. Finally, it is also likely that the degree to which
each subject expressed one or more of the nutritional abnormalities would have varied.
With particular reference to the abnormal Cu/Zn levels, 156 of 207 subjects in this cohort were found to have
abnormal Cu/Zn ratios. As shown in Table 4, the mean value for this group of 156 was presumably well
outside the normal reference range for the Cu/Zn ratio. Walsh et al. report that the “behavior disorders
associated with this imbalance include episodic rage disorder, attention deficit disorder, and hyperactivity.”
Table 4. Mean Cu/Zn ratios for ADHD group vs. Cu/Zn reference levels
GROUP
ADHD with abnormal
Cu/Zn ratios
Test standardization
population (reference
level)
MEAN Cu/Zn RATIO
1.86
STANDARD DEVIATION
0.49
0.8-1.2
However, while there are published reference ranges for minerals such as Cu and Zn, there is no published
evidence to support this particular reference range for the Cu/Zn ratio. According to Walsh (personal
communication), the determination of abnormality was based on values derived from so-called “normal”
subjects seen in his clinic, but in fact there has been no published information as to how the reference level
for the Cu/Zn ratio was actually determined. Perhaps the reference level was based on the 18 “nonassaultive” individuals who were reported in the first study, but these were still, according to the authors,
patients of the clinic—non-assaultive, perhaps, but referred for other problems [71].
11
For the high Cu/Zn ratio group, Walsh et al. developed a “metallothionein promotion therapy,” using Zn,
cysteine, and manganese (Mn), together with nutrients such as pyridoxine, ascorbic acid, and Vitamin E. But
again, there are many other unknowns, for example: (1) Whether there was a correlation between change in
Cu/Zn levels and treatment outcomes; (2) whether the individuals with improved behavior profiles (e.g., the
58% in whom assaultive behavior was “completely eliminated”) moved significantly nearer to, or within, the
normal reference range for the Cu/Zn ratio; and, 3) the number of subjects in the Cu/Zn deficiency group who
were being simultaneously treated for one or more of the other identified nutritional deficiencies. Hence,
generalizations about the importance of the Cu/Zn ratio and supplementation to normalize the ratio are
difficult to make.
Another problem with focusing on single nutrient deficiencies is exemplified by a study conducted by
Mousain-Bosc et al., in which 40 ADHD children treated with magnesium and vitamin B6 over 8 weeks were
compared with 36 ADHD children who did not receive supplementation [35]. The active treatment appeared
to reduce symptoms of inattention, hyperactivity and aggressiveness. But, the study was actually conducted
only on ADHD children who were known to have low levels of intraerythrocyte magnesium upon study entry.
Since it has never been demonstrated that low intraerythrocyte magnesium is a ubiquitous problem among
children with ADHD, we might imagine that by correcting a single nutritional inadequacy the investigators
may have perhaps raised the threshold for expression of ADHD symptoms in this particular subgroup of
children with ADHD. Whether or not the nutrient introduced directly supported the attentional system or
simply improved overall health is not well understood. As mentioned earlier, there is evidence to support the
contention that children who feel better in general may express fewer neurocognitive symptoms, of which
ADHD symptoms would be cases in point.
A more representative example of a study showing the effects of a specific class of nutrients,
polyunsaturated fatty acids (PUFAs), was recently published by Johnson and colleagues in Sweden [165]. In
this well-designed and carefully controlled study (N = 75 children age 8 to 18, all meeting DSM-IV criteria for
ADHD; randomized-double-blind, placebo-controlled trial; duration of 6 months), a majority of children with
ADHD did not respond to the treatment. However, at the conclusion of the first 3 months, a subgroup of the
34 children who completed the entire study under the active treatment condition (called “responders”)
showed a 25% or more improvement in ADHD symptoms; at the conclusion of the full 6 months, 28 of 59
subjects (the placebo group now having been being afforded the opportunity to participate in a 3-month open
label trial), 28 of 59 subjects were considered responders. Unfortunately, there were no pre or post biological
measures that might have been instructive in determining how “responding” may have been mediated.
Another satisfactory study of PUFAs was performed by Vaisman et al., with 83 children who were given both
eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), while varying the glyceride carriers [167].
The children were selected because of a specific neurocognitive sign of ADHD, namely impaired visual
sustained attention on the test of variables of attention (TOVA [167]). They found that consumption of EPA +
DHA esterified to different carriers had different effects on the incorporation of the fatty acids into blood
fraction, with a significant correlation between the alterations of PUFAs and increased TOVA scores, mainly
improved scores on the TOVA in the group in which EPA + DHA was esterified to PL-N-3
(phosphatidylserine).
Perhaps the most convincing evidence that PUFAs are related to brain function as well as neurocognitive
performance was recently published by McNamara et al., from the University of Cincinnati, who addressed
the possibility that DHA concentrations in brain gray mater regulated cortical metabolic function and cognitive
12
development [169]. Thirty-three healthy nine-year-old boys were randomly assigned to receive placebo or
one of two doses of DHA (400 or 1200 mg/d) for eight weeks. The relative changes in cortical activation
patterns during sustained the attention at baseline and after completion of treatment were determined by
functional magnetic resonance imaging (fMRI). After 8 weeks of treatment, erythrocyte membrane DHA had
increased by 47% and 70%, in those receiving low and high doses of DHD, respectively, but no change was
noted in those receiving placebo. During the sustained attention task, both DHA doses resulted in a
significantly greater change from baseline than was found with placebo in activation of the dorsolateral
prefrontal cortex, and both doses resulted in a greater decrease in activity in the occipital and cerebellar
areas. In addition, erythrocyte DHA composition correlated positively with activation of the dorsolateral
prefrontal cortex and inversely with reaction time, both at baseline and endpoint. This is the first controlled
clinical study to show an effect of DHD on functional cortical activity in human subjects. While this research is
promising, it may be difficult to replicate using children who actually have ADHD, because the fMRI segment
is not always easy to accomplish with children who are likely to be hyperactive.
In a recent review of the effects of polyunsaturated fatty acids (PUFAs) in reducing ADHD in children,
Transler et al. concluded that while the use of only one PUFA (DHA; EPA; or gamma-linolenic acid [GLA] did
not lead to a reduction of ADHD symptoms, a combination of PUFAs appeared to lead to a reduction of
ADHD symptoms; nevertheless, results on more objective cognitive test measures were inconclusive.[168] .
Critique of Single Nutrient Approaches
A major hindrance to progress in the area of nutrition-based intervention for ADHD stems from the fact that
there have been so many studies aimed at a single nutrient deficiency that investigators representing one or
another school of thought believe to be critical for regulating ADHD-like behavior. These studies are usually
based on plausible theoretical rationales, as exemplified by the brain-behavior links cited in Table 1.
Nevertheless, as shown in Table 2, there seems to be no known single nutrient with a positive outcome that
has been validated by a persistent stream of replications. Had there been a consistent pattern of replications
for any nutrient, then it is virtually certain that, whatever the intervention, it would by now have become a
standard of care for those opting for CAMs in the treatment of ADHD. Alas, there has been no standard of
care established for any single nutritional intervention, and, as mentioned earlier, the likely reason is that
ADHD is a disorder that is unlikely to be caused, or even exacerbated in most instances, by a single nutrient
deficiency, or even a small set of nutrient deficiencies.
.
Rationale for Multiple Nutrient Approach
An approach to nutritional intervention in ADHD that avoids subscribing to a belief in the benefits of single
nutrients, or in “cocktails” based on a circumscribed set of nutrients, has been proposed by Rucklidge et al.,
who have reasoned that single nutrient approaches may not be sufficient to correct all possibly relevant
imbalances, due to the vast array of nutrients required to support optimal brain chemistry [40]. Indeed, some
forms of mental dysfunction may be caused by inborn errors of metabolism whose effects are seen in key
neural systems; others may be essential in (as-yet-difficult-to-specify) for controlling epigenetic influences;
and others may be simply ascribable to inadequate nutrient intake. When measuring the level of a specific
nutrient (e.g.,Zn), in a specific case, we do not know whether a low Zn level is a cause, an effect, or an asyet difficult to specify co-occurrence of ADHD-like behavior. It makes sense, then, that a broad-based
micronutrient formula might offer the best chance of correcting and/or stabilizing metabolic activities that
support the growth and development of optimal brain architecture and neurochemistry [170].
13
A study based on the above rationale was recently published by the NEMO Study Group in Australia [171], in
which a large group of Australian (n = 396) and Indonesian (n=384) children, ages 6 to 10, participated in a
randomized, placebo-controlled study in which the active micronutrient mix contained: Fe, Zn, folate; vitamins
A, B6, B12, and C; and docosahexaenoic acid (DHD) and eicosapentaenoic acid (EPA), in a drink
administered 6 days per week for 12 months. Both the biochemical assays and the cognitive performance
measures were taken at baseline, 6 months, and 12 months. The micronutrient treatment significantly
improved plasma micronutrient concentrations, as well as scorers on test or verbal learning and memory, but
not general intelligence or measure of attention. The authors noted that the addition of DHA/EPA to the
formulas did not seem to influence general intelligence or attention.
On the other hand, a study by Sinn & Bryan [26] showed a somewhat contrary set of effects in 132 Australian
children, ages 7 through 12, with ADHD. This group found strong positive treatment effects as indicate by
ratings on Conners Parent Rating Scale scores for groups treated over 15 weeks with PUFAs, compared
with the placebo group, but no additional benefits when an additional micronutrient supplement, containing
vitamins A, B1, B2, B5, B6, B12, calcium (Ca), Fe, magnisum (Mg), Mn, Zn, Cu, and potassium (K) added to
the PUFAs. Interestingly, while parent ratings on the Conners scale improved for the PUFA groups, teacher
ratings on the parallel Conners Teachers Rating Scale did not improve. After 15 weeks, the placebo group
was crossed over to active treatment, with similarly positive outcomes as measured by the parent, but not
the teacher ratings.
The prison studies of nutrient supplementation
Perhaps the most promising demonstrations of the effects of a multiple micronutrient approach on
tendencies towards aberrant behavior have been shown in three studies of aggressive behavior in inmates of
correctional institutions [173-175]. In the earliest study, conducted by Schoenthaler and associates, the study
cohort consisted of 62 residents of a psychiatrically-oriented facility that housed juveniles in a maximum
security setting [173]. Baseline rates of violence were determined by averaging data from 13 weeks of
observations prior to intervention. The 62 residents were then matched on rates of baseline violence and
randomly allocated to either the active or placebo group (32 active; 30 placebo). Over a13 week study
period, subjects received, in pill form, either an active (12 vitamins and 11 minerals, set at or about 100% of
USRDA requirements) or a similar-appearing and tasting placebo, during morning and evening medication
rounds. Neither the researchers nor the institutional staff were aware of subject group assignments. The
resulting difference in violent behavior between active and placebo groups was reported as 28%. Based on
pre-post analysis of blood concentrations of vitamins/minerals, it appeared that subjects who corrected
abnormally low blood vitamin and mineral concentrations during the course of the study benefitted more from
the intervention that those who did not show such corrections.
The second study, conducted by Gesch and associates in the UK, was based on the same nutritional
approach [174]. The subjects were 231 young adult prisoners who were provided with either 1) a standard
commercial vitamin/mineral supplement as well as linoleic acid, gamma linoleic acid, eicosapentaenoic acid,
and docosahexaenoic acid (n = 116) or 2) vegetable-oil based placebo (n =115). In an “intent-to-treat”
design, 82 active and 90 placebo subjects were analyzed, all of whom were treated for a minimum of 2
weeks and up to 9 months (average treatment time = 142 days). The active nutrient group achieved a
significant (p<0.005) reduction in the most serious incidents of aberrant behavior, including violence.
14
The most recent of these studies, conducted by Zaalberg and associates in The Netherlands, was essentially
a replication of the Gesch et al. study with prison inmates, but with a more generous allotment of PUFAs in
the active supplement, and some noteworthy enhancements of study design [175]. In this study, 221 male
participants were randomized to active and placebo conditions, and took their respective capsules for no less
than 30 days, and up to three months (average 75.9 days). There was no significant difference between the
active and placebo groups with respect to average number of participation days. With respect to the number
of reported incidents of disruptive behavior reported by prison staff, expressed in terms of incidents per 1,000
prison days, the active supplement group had a 34% reduction, while the placebo group had a 14% increase
in such incidents--comparable to the outcome of the Gesch study. Zaalberg et al. noted, however, that it
would have been desirable to have been able to a) monitor total dietary intake and b) periodically assay the
levels of micronutrients present in blood samples. They further noted that it was possible that a significant
number of their subjects may have also experienced more nutritious diets in the prison setting than before
incarceration, especially since it is likely that a substantial proportion of the group had been alcohol/illicit drug
abusers. (But, it has been shown by Eves and Gesch [176] that even within prison settings, inmates often
continue to make poor dietary choices, though sufficiently nutritious meals are available.)
The three prison studies cited above are encouraging with respect to the possibility that micronutrient
supplementation could act to reduce maladaptive behaviors in children and adolescents, as evidenced by the
presence of significant behavioral changes, even in late adolescent/young adult populations. But, there are
several problems with these studies. First, among prisoners in general, baseline rates of violent behavior
tend to be rather low. In the Schoenthaler study, for example, base rates of violent rule infractions for the
active and placebo groups were 0.389 and 0.372 per week, respectively—this, in a study in which subjects
may have been followed for as few as 4 weeks and no more than 13 weeks. The actual figures showing
change in violent rule infractions for the Schoenthaler study are shown in Table 5. As is evident from the
table, the group receiving active treatment had a 96% reduction in violent offenses; but, the group receiving
placebo had a 67% reduction—a rather astounding placebo effect, until one considers the problem posed by
departures from very low baseline rates.
Table 5. Mean rates per subject for violent rule infractions by group and condition. (from Schoenthaler, 1997)
Group
Active supplement
Placebo
Baseline
0.389
0.372
Post-treatment
0.078
0.163
Net Change
0.311
0.209
The Zaalberg study employed a more sophisticated metric, taking into account the variation in length of study
participation by converting staff-observed maladaptive behaviors into incidents per 1,000 days. Nevertheless,
the average baseline rate at the onset of the study was a mere 11.0 incidents per 1,000 days (a probability,
then, of about 1% per day); further, a majority of offenses were committed by a small segment of the
population, while “many subjects were not involved in incidents.”
None of the above authors took into account another potential confounder, namely that the general social
milieu of prison life might have served to progressively suppress maladaptive behaviors, as a function of
duration of incarceration. In both the Gesch and Zaalberg studies, for example, there seems to be an implicit
assumption that baseline rates of maladaptive behavior were likely to have remained static over the course
of the study, had it not been for the intervention-- an assumption which seems on its face to be
15
counterintuitive. Indeed, any presumed placebo effect must be weighed in the context of a likely change in
likelihood of maladaptive behaviors persisting over time.
The Placebo Effect
A ubiquitous occurrence during many studies of nutritional supplementation is a significant placebo effect.
While the size of such effects may be slight in comparison to the magnitude of change attributable to the
active treatment condition, it is still legitimate to question how a whether the placebo effect is actually a
placebo-generated departure from baseline, or whether some other factor might be influencing the observed
change over time, for example, a) the passage of time in a controlled environment, b) a relatively more
nutritious diet in contrast to the pre-incarceration diet, or c) the simple result of biological maturation.
Consideration a) is somewhat difficult to specify, although it is commonly accepted that the presence of a
“prisoner government,” as well as the efforts of correctional programs and growing relationships with staff
may well serve to tone down the propensity for disruptive behavior over time. Consideration b) might be best
investigated by contrasting dietary histories at the time of incarceration with a record of the consumption of
nutrients in the prison setting. The effects of biological maturation might only be determined in cases of
incarcerations of such duration as to encompass a significant increment in neurobiological maturation. In any
event, a study in which the course of development for a group that is afforded neither the active supplement
nor the placebo is long overdue. It represents a way of determining the true strength of the placebo effect.
C. PRELIMINARY STUDIES
The UCI Child Development Center
[Sabrina, et al: While we obviously have no track record in nutritional intervention studies, I think
that we should emphasize in this section the fact that we have been a NIH-selected study site for
two major studies, as well as the more recent HAI study, and that we have the infrastructure in
place to support recruitment, assessment, management of treatment schedules, data collection
and analysis. I would also describe the virtues of the Social Skills Training Programs to be used in
this study, and our proficiency at measuring outcomes.]
By way of example:
16
“For more than 25 years, the UCI CDC has conducted research on the diagnosis and treatment of ADHD
(best references???) We have been involved in the development of new drug delivery systems to improve
the use of stimulant medication (best references???), including large-scale trials to compare and contrast
the benefits of medications vis-a-vis behavioral interventions in school-aged children with ADHD (best
references????) and preschool children (best references???). Based on these experiences, we believe
we have developed a unique capacity to evaluate the benefits and limitations of stimulant medications
and many non-pharmacological interventions, which should serve us well in conducting the proposed
study.”
1. MTA Study. One of the on-going studies was the Multimodality Treatment Study of ADHD (MTA), etc.,
etc.
2. Preschool Study.
3. HAI Study. (Here, we should highlight our transitioning into non-drug, CAM approaches.
D. DESIGN OF PROPOSED STUDY
1.0 GOALS AND OBJECTIVES
The primary goal of the proposed study is to assess the efficacy of nutritional supplementation for children
with ADHD, using a multiple micronutrient formula. A secondary goal will be to determine the strength and
persistence of an often-reported placebo effect. The specific objectives are as follows:
1.1 Recruit up to 150 children, ages 7 through 12, suspected of having ADHD. Obtain informed consent
from legally authorized representatives for no fewer than 120 children who meet DSM-IV-R diagnostic
criteria for ADHD (see section 1.5.1, below).
1.2 Randomly assign the 120 subjects to one of three treatment groups:
1.2.1. SSTP + Micronutrient supplement group (Group I; n = 40)
1.2.2 SSTP + Placebo supplement group (Group II; n = 40)
1.2.3. SSTP only control group (Group III; n = 40)
1.3 Approximately eight weeks after enrollment, assign each subject, irrespective of experimental group
assignment (see above), to a SSTP, which will then continue for 12 weeks.
1.4 At conclusion of SSTP (at approximately the 20th week following enrollment), make the active
micronutrient formula available to all subjects, and continue following through the 32nd week.
1.5 Perform specific behavior rating scales at the following times:
1.5.1 Intake
1.5.1.1 SWAN Scale (Diagnostician): Week 1
1.5.1.2 KSADS (Diagnostician)
17
1.5.1.3 Abbreviated WISC-IV; Week 1
1.5.1.4: SWAN Scale (Parent[s]); Week 1
1.5.2 Over course of randomized assignment (Week 1 through Week 20)
1.5.2.1 SSIS-RS (by SSTP training staff); weekly, weeks 8 through 20)
1.5.2.2 SSIS-RS (by parent[s]); weeks 8 through 20
1.5.3 At conclusion of open-label period
1.5.3.1 SSIS-RS (by parent[s]); Week 32
1.5.3.2 SWAN scale (by parent[s]); Week 32
1.6 Laboratory neuropsychological battery
1.6.1 Pediatric CMinds battery (by neuropsychology technicians): Weeks 1, 20, and 32
2.0 Study Population
The study population will consist of 120 male and female children with a primary diagnosis of ADHD,
hyperactive/impulsive or combined subtype, as defined in the Diagnostic and Statistical Manual of Mental
Disorders, Fourth Edition, Revised (DSM-IV-R).
2.1 Criteria for ADHD diagnosis:
Children selected for enrollment must meet DSM-IV-R criteria for ADHD as confirmed by a
preponderance of derived from the following forms of assessment:
2.1.1 Clinical interview, from which the study psychiatrist, Dr. Schneider, will make a provisional
diagnosis based on a) information gathered through family history questionnaire, parent narrative,
and academic performance records, as well as the following standardized procedures:
2.1.1.1 KSAD-S inventory
18
2.1.1.2 SWAN scale ratings (Parent and diagnostician)
2.1.1.3 Abbreviated WISC-IV
2.2 Additional Inclusion Criteria:
Children who meet the criteria for diagnosis of ADHD, as described in the preceding paragraph, will
qualify for entry in the study if they also satisfy the following criteria:
2.2.1 The family must provide signature of informed consent by: a) both parents; b) a parent
having legal sole custody; or c) a legal guardian.
2.2.2 Age at study entry no younger than 84 months nor older than 144 months.
2.2.3 Cognitive Functioning: IQ of greater than 70 as estimated by abbreviated WISC-IV
2.2.4. Parent/Caretaker: Child must have been residing with the current primary caretaker
for at least 6 months prior to screening, and will be likely to continue in the same household
over the duration of the study (32 weeks).
2.2.5. Both child and at least one parent/caretaker must be English-speaking
2.3 Exclusion Criteria:
Potential subjects will be excluded if:
2.3.1 The subject and parent cannot understand or follow instructions given in the study.
Since the instruments used to rate progress in the Social Skills Training Program have not
been a) translated into languages other than English, and b) standardized for non-English
speaking populations, children of non-English speaking parents/caretakers will be excluded.
2.3.2 The patient has a known allergy to any vitamin, mineral or essential fatty acid.
2.3.3 The subject has a primary diagnosis of a major psychiatric disorder other than ADHD
and closely related conditions (e.g., oppositional defiant disorder [ODD]; dyslexia).
Disqualifying conditions would include diagnoses such as: dysthymia, generalized anxiety
disorder, bipolar disorder or pervasive developmental disorder).
2.3.4 The subject will using any psychoactive medications for management of behavioral
problems, including ADHD (see Table 6, below, for list).
2.3.5 The subject regularly takes medication(s) that could have psychoactive effects, even if
for general medical conditions (e.g., beta blockers for heart disease, synthetic thyroid for
hypothyroidism; anticonvulsants for seizure disorders). Episodic use of decongestants for
the common cold or PRN use of antiasthmatics would be allowed.
19
Table 6. Excluded Medications:
DRUG CLASS
Antibiotics
Antiasthmatics
(systemic/ventilator)
Antiasthmatics (topical)
Antacids
Antidiarrheals
Antihistamines (Seldane type)
Antihistamines (mixed
antagonists, e.g., Benadryl)
Antinauseants
Aspirin
NSAIDS
Non-narcotic analgesics
Narcotics
Cough/cold preparations
Laxatives
H2 Blockers (e.g., anti-ulcer
meds)
Hormones
Anti-seizure medications
Anti-hypertensives
Diuretics
Steroids
Antiarrhythmics
Hypoglycemic agents
Insulin
Tryptophan
Methylphenidate in various forms
(e.g., Concerta)
Amphetamines in various forms
(e.g. Adderall)
Antipsychotics (e.g.,
phenothiazines)
Mood stabilizers (e.g., valproaic
acid; lithium)
Antidepressants (e.g., SSRIs)
Anxiolytic drugs (e.g.,
benzodiazapines)
EPISODIC USE
O.K.
O.K.
CHRONIC USE
O.K.
No
O.K.
O.K.
O.K.
O.K.
O.K.
O.K.
O.K.
No
O.K.
No
O.K.
O.K.
O.K.
O.K.
O.K.
O.K.
O.K.
O.K.
No
O.K.
O.K.
O.K.
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
3.0. Micronutrient/Placebo Supplementation Program
20
On a weekly basis, parents whose children have been randomized to either the active supplement or
placebo condition will receive a week’s supply of the active nutrient or the placebo. The active micronutrient
capsule(s) will be formulated in a manner as to essentially replicate those described in the studies by Gesch
et al. [174] and Zallberg et al. [175], as shown in Table 7.
Table 7. Potencies of micronutrients and to be used in the present study, contrasted with those reported
by Gesch et al. [174], Zaalberg et al. [175], and US DRIs.
21
POTENCY
Nutrient
Form
Proposed Zaalberg Gesch
study
et al.
et al.
[174]
[175]
Vitamin A ug
Retinol Acetate
750
750
750
Vitamin B1 mg
Thiamine
1.2
1.2
1.2
Vitamin B2 mg
Riboflavine
1.6
1.6
1.6
Vitamin B3 mg
Nicotinamide
18
18
18
Vitamin B5 mg
Calciumpanothenate 4
4
4
Vitamin B6 mg
Pyrodoxal-52
2
2
phosphate
Vitamin B11 ug
Folic Acid
400
400
400
Vitamin B12 ug
Cyanocobalamine
3
3
3
Biotin ug
100
100
100
Vitamin C mg
Ascorbic acid
60
60
60
Vitamin D3 ug
Cholocalciferol
5
5
10
Vitamin E mg
D-alfa-tocoferyl
10
10
10
acetate
Calcium mg
Tricalcium
100
100
100
phosphate
Magnesium mg
Magnesium citrate
100
100
30
Phosphorus mg
Tricalcium
100
52
77
phosphate
Zinc mg
Zinc citrate
15
15
15
Iron mg
Ferro fumerate
12
12
12
Manganese mg
Manganese
3
3
3
gluconate
Copper mg
Copper gluconate
2
2
2
Potassium mg
Potassium chloride
4
4
4
Iodine ug
Potassium iodide
100
140
140
Selenium ug
Sodium selinite
50
50
50
Chromium ug
Chromium chloride
200
200
200
Molybdenum ug
Sodium molybdate
250
250
250
Docosahexanoenic
200
400
44
acid mg
Eicosapentainoic
400
400
80
acid mg
Gamma-linolenic
100
100
160
acid mg
Linoleic acid
--1,260
 US Daily recommended intake
Recommended
US DRIs
900
1.2
1.3
16
5
1.3
400
2.4
30
90
5
15
1,000
400
700
11
8
2.3
0.9
150
55
35
45
-----
Hero Nutritionals, Inc., of San Clemente, California, will be manufacturing the nutrients to be used in the
proposed study, so that their content will be essentially similar to that of the capsules used in the Zaalberg
et al. study, as shown in Table 7. Hero nutritionals will also manufacture the placebos so that they will
22
have the taste and appearance of the active nutrient. By way of background, Hero Nutritionals has
specialized for many years in providing children’s nutrients that are known for their visual and taste appeal,
marketed under the label of “Yummy Bears.”
Parents will be instructed to provide the vitamin-mineral supplements (or placebo), in “Yummy Bear” form at
or around breakfast time. Children in Group I will take the active supplement over the 32-week course of the
study; children in Group s II will take the placebo through week 20, and the active supplement through week
32; children in Group III will take the active supplement from week 20 through week 32.
23
5.0 Assessment Techniques
5.1. SWAN scale ratings (???Sabrina: description of SWAN scales here)
5.2. SSIS ratings during Social Skills Training Program
During the 12-week SSTP, subjects will be rated a) weekly, by their parent(s), and b) at the conclusion of
each SSTP session by the SSTP training staff, using the Social Skills Improvement System Rating Scales
(SSIS-RS), a rating methodology that consists of standardized, norm-referenced rating items that can be
uses with children from preschool through secondary school (see Appendix A for copy of rating forms).
The SSIS-RS is the featured assessment component of the Social Skills Improvement System, which also
includes the SSIS Classwide Intervention Program, the SSIS Intervention Guide, and the SSIS
Performance Screening Guide [177, 178]. To a great extent, the SSIS serves as the basis for the content
of the SSTP that is offered as a standard clinical service at the UCI Child Development Center.
The SSIS-RS consists of multi-rater assessments of a broad range of behaviors that are known to affect
teacher-student/parent-child relationships, peer interactions, and academic performance, and has been
found to be a useful tool for evaluating the effectiveness of SSTPs. The instrument documents the
perceived frequency and importance of behaviors reflecting social competence in home, school and
community settings, as indicated by ratings in seven domains of Social Skills:
a.
b.
c.
d.
e.
f.
g.
Communication
Cooperation
Assertion
Responsibility
Empathy
Engagement
Self-control
Also rated are five domains of competing Problem Behaviors:
a.
b.
c.
d.
e.
Externalizing
Bullying
Hyperactivity/Inattention
Internalizing
Autism spectrum
Because of a) the reciprocal relationship between Social Skills and Problem Behaviors items, b) the
administration time per individual (15 to 20 minutes for a full SSIS-RS), and c) the frequency of ratings
demanded by the study design, only the SSIS-RS Social Skills domain items will be rated in the proposed
study.
The reliabilities for the SSIS Social Skills functioning domain, for the age ranges to be included in this
study, are shown in the following table:
24
Table 8. Reliability studies of Social Skills Domain ratings on SSIS-RS (from Gresham & Elliott [178]).
INTERNAL CONSISTENCY: COEFFICIENT ALPHA, AGES 6-12
FORM
Teacher
Parent
Communication
.97
.95
Cooperation
.86
.74
Assertion
.91
.83
Responsibility
.83
.84
Empathy
.91
.86
Engagement
.92
.83
Self-Control
.90
.84
TEST-RETEST RELIABILITY: ALL AGES
Communication
.76
.83
Cooperation
.86
.80
Assertion
.74
.76
Responsibility
.82
.82
Empathy
.78
.78
Engagement
.83
.86
Self-Control
.86
.80
Validity of the SSIS Rating Scales has been demonstrated by observing the correlation of scores on its
subscales with those found on rating test of similar content, such as the BASC-2 and Vineland II [ 179,
180]. The overall correlation between the Social Skills scales of the SSIS-RS and similar content scales of
the BASC-2 for two instruments was found to be .78 for children in the age 5 to 12 cohort [Gresham and
Elliott, 2008]. A similar study correlating Social Skills Domain ratings of children on the Vineland-II
Teacher Rating Form (VTRF-II) with the Social Skills domain ratings on the SSIS-RS showed the
following:
179
VTRF-II Communication Domain: .60
VTRF-II Daily Living Skills Domain: .69
VTRF-II Socialization Domain: .65
VTRF-II Adaptive Behavior Composite: .68
We currently use the SSIS-RS for tracking the weekly progress of social skills acquisition for children
enrolled in our CDC School Program, which has a daily SSTP, as well as for children involved in the NIHfunded HAI Project (ref???). Before ratings commence in the proposed study, both parents and SSTP
trainers will be given to ensure that discrete, observable behaviors are linked to the SSIS-RS rating
categories.
5.3 Laboratory measures of neurocognitive functioning
A battery of computer-generated and scores tests of attention, memory and learning (CMINDS) has been
developed by James O’Halloran, Ph.D. and associates at Neurocomp, Inc., in Newport Beach, California
[181]. The tests are implemented on a unique, dual-monitor, test-administration platform that enables the
examiner to closely monitor, guide, and interact with the subject in real time, without requiring intrusive
“over-the-shoulder” administration procedures (see Fig 2). The dual-display platform allows the examiner
25
to supplement prerecorded instructions by repeating or rephrasing as needed by the subject and as
permitted by the established rules of administration. During a testing session, the examiner is able to
control the pace of testing, pause, repeat instructions, and receive continual advisories on subject
performance and test progress.
Figure 2. schematic representation of CMinds pediatric battery (from O’Halloran et al., [181])
The six discrete neurocognitive assessment tests are described below.
5.3.1. Go/No-Go Task
A variant of the Continuous Performance Test (CPT) initially developed by Rosvold and associates [182].
The Go/No-Go Task requires the execution of an anticipated motor response or its active inhibition. A
child’s inhibitory ability is a measure of impulsivity and is associated with executive functioning.
Participants are primed to press a button as quickly as possible in response to the presentation of every
letter of the alphabet, except for the letter “X”. In this task, there are two blocks of trials. The first block
consists of 25 trials containing 100 percent target stimuli, used to prime participants to respond to the
target stimuli. The second block is the response inhibition condition, consisting of 50 trials containing 25
target stimuli (Go trials) and 25 non-target stimuli (No-Go trials). Scores derived from this test include
percent correct, mean reaction time (RT), and the speed-accuracy or “inverse-efficiency” ratio (RT/percent
correct) for each block. An additional measure of overall performance on this test is the discriminability
index (d’), which is calculated as the difference between standardized “hit” and “false-alarm” rates.
5.3.2. CPT-Flanker
The Flanker task is also a variant of the CPT which measures executive functioning and requires the
ability to resolve conflicts when competing information is present [183]. Participants view five arrows and
are instructed to press a left or right response button based on the direction of the center arrow (target).
They are instructed to ignore the surrounding arrows which are either congruent or incongruent with the
center arrow. This task consists of 24 congruent trials and 24 incongruent trials. Each set of arrows is
presented until the child responds (maximum of 5000 msec) with a 750 msec inter-trial interval. Scores
derived from this test include percent correct, median RT, and the inverse-efficiency ratio for both
congruent and incongruent trials. The “congruency effect” is also calculated as the difference between
congruent and incongruent trials for all three of these measures.
26
5.3.3. Sequential Memory Test (SMT)
The Sequential Memory Test, based on the Corsi block-tapping test [184], assesses children’s capacity
for holding a visual-spatial sequence in working memory and involves active rehearsal and controlled
attention [185]. At the start of the task, squares on a computer monitor change color for 1000 msec, one
at a time, in a predetermined sequence. The child is then asked to reproduce the sequence by touching
the squares in the order that they change color. The length of the sequence increases with accurate
performance. The test is terminated after two consecutive errors at a given sequence length. Scores
derived from this test include total number of correct and incorrect trials and the longest sequence
correctly reproduced.
5.3.4. Set Shifting Test (SST)
This task measures the ability to first learn a simple pattern and then abandon it when it is no longer
reinforced, in favor of a new pattern [186]. Specifically, children are instructed to "guess" into which
doghouse an animated dog will move. After a choice is made, the dog moves according to a
preprogrammed pattern. The first pattern is “single alternation” (Left, Right, Left, Right). After the child
correctly learns this pattern, as indicated by guessing the correct house 10 times in a row, the pattern
changes to “double alternation” (Left, Left, Right, Right). Scores derived from this test include the percent
correct and “failures to maintain set” for both single- and double-alternation trials.
5.3.5. Continuous Recognition Memory Test (CRMT)
The Continuous Recognition Memory Test (CRMT) employs nonverbal, visual stimuli (both “concrete” and
“abstract”) to assess the child’s ability to discriminate between previously presented ("old") and “new”
stimuli. The CRMT includes pictures of common objects that can be verbally labeled (“concrete” stimuli)
and shapes that are not as easily labeled verbally (“abstract” stimuli), which were developed by Charles
A. Nelson (Department of Pediatrics, Harvard Medical School) and colleagues. Half of the 210 trials
display concrete and half of the trials display abstract stimuli and are presented for 500 msec, with 2000
msec intervening between lags. Eighty stimuli each are presented twice at either a lag of two or five. The
remaining trials comprise 50 distracter stimuli (not analyzed) that either are not repeated or are repeated
at non standard intervals. Previous brain imaging research using this paradigm has shown activation in
the hippocampus while performing this task [187. 188]. Scores derived from this test include percent
correct, mean reaction time (RT), and the speed-accuracy or “inverse-efficiency” ratio (RT/percent
correct) for each trial type. An additional measure of overall performance on this test is the discriminability
index (d’), which is calculated as the difference between standardized “hit” and “false-alarm” rates.
5.3.6. Finger Tapping Test (FTT)
The FTT is used to evaluate lateralized motor functions as well as the ability to sustain motor functioning
over a short time and is a common measure included in most neuropsychological assessment batteries
[189, 190]. The test requires the child to tap a button with his or her index finger as fast as possible for 10
seconds without moving his or her arm or wrist. A total of 6 trials are administered (3 for each hand).
Scores derived from this test include the number of taps for each trial and the mean number of taps for
each hand.
27
6.0 Study Schedule
The schedule of study activities over the 33 study weeks is shown in Table 8.
Table 8. Schedule of activities over 33 weeks of proposed study
PROCEDURE
Enrollment
Medical/psychosocial history
Physical examination
Diagnosis
Legal informed consent
Assignment to respective nutrient group
Ramp-up period
Social Skills Training Program (SSTP)
Nutritional Supplementation Condition
Group I
Group II
Group III
Assessments
SWAN (Clinician)
SWAN (Parent[s])
SSIS (Parent[s])
SSIS (Trainer)
Laboratory Neurocognitive Assessment
Abbreviated WISC-IV
Pediatric CMINDS Battery
Week
1
1
1
1
1
2-9
Weekly;
10-21
Active; week 2-33
Placebo; week
Active; week 22-33
2-21
None; week 2Active; week 22-33
21
1
1 9
1
21
Weekly; 9-21
Weekly; 9-21
1
1
22
33
33
33
33
7. Proposed Statistical Analysis
7.1 Database management.
Management of results will involve structuring all data into a uniform database format for ease of analysis
and quality assurance. Quality control will consist of several steps. First, frequency distributions will be
compiled for all variables and distributions then inspected to identify out-of-range values. This process will
be repeated until no more errors can be identified. The resulting information will be maintained in an Excel
format, which can interface with the input requirements of the various statistical packages to be employed
(e.g., SPSS 15.0). A codebook will be developed to assure that data fields for variables are properly
identified.
7.2 Confidentiality of database.
28
All data will be labeled using identified a unique identifier code (UIC) which will not contain the subject’s
name or any other clue that could be linked to individual subject identity. The research staff will sign an oath
of confidentiality at the outset of their employment on the project with the understanding that civil and criminal
penalties could result from divulging any information about research subjects, whether personally identifiable
or not. The data will be compiled and analyzed in such a way that subject anonymity will continue through
dissemination of results..
7.3 Statistical. Analysis.
7.3.1 Dealing with attrition bias.
In any longitudinal study, threat to the validity of the findings could arise as a result of participant attrition.
Should the loss of participants occur in a systematic way, the safeguards of randomization built into the study
design could be compromised and threaten the validity of the findings. Subject demographics will be used to
determine if pre-existing characteristics of subjects are related to attrition, which will enable us to determine
the representativeness of the remaining sample.
7.3.2 Measuring and modeling individual change within context.
Recent years have witnessed major advances in the statistical methods available for the quantitative
analysis of longitudinal data, such as those collected in this study. Familiar statistical techniques, such as
analysis of variance or multiple regression analysis, are ill-suited for addressing questions of change over
time. As pointed out by Willett et al. [191], cross-sectional designs tell us nothing about patterns of change
and event occurrence. In this study, for example, does change occur immediately after children are given
nutritional supplements, or later? What is the trajectory of the placebo response? Does it differ from the
response to active treatment or to neither placebo nor active treatment? Individual growth curve modeling
presents the opportunity for us to answer such questions.
In the present study, we intend to use latent growth curve modeling based on structural equation modeling
methods [192]. This will enable us to describe the form of changes over time. Additionally, it will enable us to
understand the variability in change, for example, why some children may have better outcomes than others
when given nutritional supplements. Using latent growth curve modeling, we are able to answer the following
questions:
1. What happens over time? Is growth in social skills acquisition (growth) linear? Are there plateaus? Can
we develop a growth model that fits the data?
2. Where does the process start? What is the initial level (intercept)?
3. How rapidly does the process develop? Is there a steeper slope at some point? If development of social
skills is nonlinear, when does the direction change most?
4. What accounts for the rate of growth?
5. Does the rate of change in one attribute relate to the rate of change in another?
29
8.0 ethical justification for placebo
This is a 33-week study of the efficacy of vitamin-mineral supplementation in children with ADHD. Since
all subjects in this study will have been diagnosed with ADHD, yet precluded from taking the currentlyaccepted most efficacious treatment for ADHD, stimulant medication, for a period of 33 weeks, all
subjects will be enrolled in a 12-week social skills training program, which has been shown to have a
modest benefit for ADHD children, even when non-medicated (Sabrina: reference????).Thus, no child in
the study will go entirely “untreated.” Furthermore, over the 33 week course of the study, all subjects will
have to opportunity to use the active nutrient for a period of 12 weeks.
The rationale for the use of three nutritional supplementation groups is as follows: First, we will be able to
contrast the effects of an active micronutrient supplement with a placebo, in order to determine if there is
a significant difference in group outcomes with active supplement. Second, the children and their families
in Group III will be aware of the fact that they are receiving neither active supplementation nor placebo,
thereby enabling us determine the strength of a highly likely placebo effect by contrasting Group II
outcomes at week 20 with those of Group III at the same time.
G. HUMAN SUBJECTS
1. Potential study subjects will be selected from the pool of ADHD children who will have responded to
standard study announcements (see Appendix???).
2. Procedures performed will include nutritional supplementation, physical examination, clinical
interviews, rating scales, and neuropsychological tests.
3. Method of soliciting participation. Subjects will be asked to volunteer for the study. They will not be
paid for participation.
4. Risks. There are no known risks of vitamin/mineral/fatty acid supplementations at the levels proposed.
There are no known risks associated with social skills training programs, with the possible exception of
occasional discomfort over conflicts with peers that might occasionally occur. These tend to be minimal
because of the standardized format employed and the level of training of the SSTP staff. Subjects/families
may feel some discomfort during the interviews or test administrations. There will be a risk of breech of
confidentiality by study personnel, which will be minimized by training an making sure that staff access to
subject data remains on a “need to know” basis.
5. Methods of minimizing risks. All project staff will be carefully trained in methods of interviewing and
data collection. The Principal Investigator will periodically spot-check the research staff to ensure that
research subjects are not being subjected to inordinate stress or discomfort. All personal information will be
stored under a unique identifier, with subject names kept in a separate file, away from any personal data
pertaining to them.
6. Subjects will be informed that participation in this project is voluntary, and that refusal to participate will
involve no penalty, loss of services, or addition to the length of their probation. Subjects will also be informed
that they may withdraw entirely from the study at any point in time without penalty or reprisals. Since data
collected prior to withdrawal from the study or drop-out from the treatment program will be germane to
questions of sample representativeness, data collected form subjects up until the time of withdrawal and/or
program failure will be analyzed for purposes of understanding the representativeness of the remaining
sample.
F: VERTEBRATE ANIMALS
30
N/A
G: CONSULTANTS and COLLABORATORS
N/A
H: CONSORTIUM/CONTRACTUAL ARRANGEMENTS
The proposed study is a cooperative effort between the UCI Child Development Center and the Violence
Research Foundation, a non-profit charitable corporation based in San Clemente, California..
31
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