131049 MSc PNRD

Autism: The relationship between gut
bugs and the brain
This dissertation is submitted as part of the requirement for the
Master of Science (MSc) degree
131049
MSc Personalised Nutrition
CNELM
7th
April 2015

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Abstract
Objectives
It has long been noted that gastrointestinal complaints are a co-morbidity
associated with Autism. In addition to this neurotoxins produced as a result of
pathogenic gut bacteria have been acknowledged for their ability to produce
neurotoxins as well as neuroinflammation. Neuroinflammation and active immune
systems are also commonly spoken about within the field. The main objective was
to explore the mechanisms behind these three areas and assess how they may
contribute to Autism either as an instigator or as a result of pathogenesis. The main
objectives of this study were:
• To carry out a literature review on gut bacteria, neurotransmitter function, and
the immune system
• To carry out a retrospective observational study using patient medical records
from a private medical health clinic specializing in Autism.
• To use the information concluded from both the review and study to propose
future research and recommendations for clinical practice.
Methods
• A systematic review of the literature was conducted primarily using Pubmed.
• A retrospective observational study on autistic males under 13 was conducted
by extracting private functional health tests from patients’ medical records.
Statistical analysis was done via SPSS.
Results
In autistic males under 13 it was found that:
•Adiposity Index was significantly elevated when compared with the population
mean.
•There was no correlation between adiposity index and bacterial markers as
provided by the Organic Acids Test
•2HydroxyHippuric Acid, HVA & VMA and the Quinolinic Acid/ 5-HIAA ratio
were all significantly elevated in autistic children when compared with the
population means.
•4-Cresol, 5-HIAA and Citramalic were all significantly lower when compared
with the population mean.
•Hippuric Acid, DHPPA, HPHPA, Quinolinic Acid and KYNA were not
significantly different from the population means.
•Hippuric Acid, HPHPA, Succinic Acid and Citramalic all positively correlated
with one or more of the immune markers; White Blood Cells, Eosinophils
and Lymphocytes.
• Hair zinc was significantly lower than the population mean
• Hair zinc was positively correlated with succinic acid and Eosinophils.

Autism: Gut Bugs and The Brain 131049
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Conclusions
It is still unclear whether pathogenic gut bacteria are a necessary component of
autism, due to the differences in strains found. It is likely that these are age
dependent. It is clear however that there is a strong relationship between gut
inflammation and neuroinflammation. Post hoc analysis supported these
hypotheses. From the literature review it appears that genetic deficiencies in
carbohydrate digestion leave the child vulnerable to pathogenic gut bacteria, which
is then able to induce neurotoxicity and/or neuroinflammation. However future
research is still necessary. The literature does suggest that addressing these in
autism leads to a reduction in symptoms. Elevations and Decreases in organic
acids markers provide opportunities in clinical practice to address these with
nutritional supplements. Further research in the ability of these to reduce Autistic
symptoms is still needed.

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Table of Contents
Abstract ...................................................................................................................2
Table of Contents .................................................................................................4
List of Tables and Diagrams..………………………………………….………………...5
Preface/Acknowledgements……………………………………………………………...6
Abbreviations……………………………………………………………………………....7
Glossary…………………………………………………………………………………....9
Introduction..……………………………………………………………….………......…16
Objectives………………………………………………………………………..18
Chapter 1: Literature Review……………………………………………………………18
1.1. Methodology……………………………………………………………..18
Findings…………………………………………………………………..20
1.2. The Relationship between Gut Bacteria and Autism………………..20
1.3. The Relationship between Gut Bacteria and Neurotransmitters…..23
1.4. The Relationship between Gut bacteria, Neuroinflammation and
Immune Activation……………………………………………………...27
Chapter 2: Study…………………………………………………………………………33
Research Hypotheses…………………………………………………………33
2.1 Methodology……………………………………………………………….33
2.2 Results……………………………………………………………………..36
2.3 Discussion of Study Findings…………………………………………….42
2.4 Implications………………………………………………………………..45
2.5 Strengths and Limitations………………………………………………..46
2.6 Future Directions………………………………………………………….47
Chapter 3: Conclusions…………………………………………………………………47
References……………………………………………………………………………….49
Bibliography………………………………………………………………………………57
Appendices……………………………………………………………………………….58

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Lists of tables & figures
Number Title Page
Table 1 Search Terms and Filters used for
Literature review
19
Table 2 Taxonomic Examples of Bacteria
from the Intestine
27
Table 3 Age and Gender Specific
Population Means
34
Table 4 Descriptive Statistics for AU
Males<13
35
Table 5 Descriptive Statistics – Percentiles
for AU Males <13
36
Table 6 OATS in AU Males<13 Compared
with the Population Mean –
Significant Findings
39
Table 7 Bacterial and Immune Markers in
AU Males< 13 – Significant
Findings
40
Table 8 Post Hoc Analysis: Bacterial OAT
Markers and 5-HIAA – Significant
Findings
41
Table 9 Post Hoc Analysis: Bacterial OAT
Markers and Quinolinic Acid –
41
Table 10 Table of Hypotheses 42
Table 11 Nutritional Interventions based on
OAT Markers
45
Figure 1 Variable Insult Model 17
Figure 2 Systems-Based Computation Model
of the Gut Microbiome and
Regressive Autism
24
Figure 3 Mechanism of probiotic treatment in
Autism 30
Figure 4 Proposed Mechanism Derived from
the Literature Review
32
Figure 5 Histogram of Adiposity Index in AU
Males<13
38

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Preface/ Acknowledgements
Autism and the related disorders affect thousands of children. Research in this
area is ever emerging as the patho-physiology of the disease is further explored. It
is hoped that the research provided will help these children benefit in terms of both
prevention and treatment of the disease. It has been a pleasure to contribute to this
field, in even a small way. This work is a result of a study conducted as part of my
Master of Science degree course in Personalised Nutrition.
I would like to express my sincere gratitude to those who have made the
completion of this dissertation possible: Dr Daniel Goyal and all the team at
Sincere Health. Dr James Neil; Mark Howard at Biolab Medical Unit, UK; the
families in attendance at Sincere Health and Claire Sehinson.

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List of Abbreviations
AI Adiposity Index
ANS Autonomic Nervous System
AU Autism
ASD Autism Spectrum Disorder
BBB Brain Blood Barrier
BDNF Brain-derived neurotrophic factor
CDC Centres for Disease Control
CHARGE Childhood Autism Risks from Genetics
and Environment
CNELM Centre for Nutrition Education and
Lifestyle Management
CNS Central Nervous System
COX-2 Cyclooxygenase
DZ Dizygotic
ENS Enteric Nervous System
FITC Fluorescein isothiocyanate
GI Gastrointestinal
GF Germ Free
HMGB1 High-mobility group protein B1
HPA Axis Hypothalamic–pituitary–adrenal axis
IFN γ Interferon gamma
IL-1 Interleukin 1
IL-1β Interleukin 1 Beta
IL-4 Interleukin 4
IL-6 Interleukin 6
IL-12 Interleukin 12
KYNA Kynurenic acid
LPS Lipopolysaccharide
mRNA Messenger RNA
MZ Monozygotic

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NDD Neurodevelopmental Disorder
NHS National Health Service
NMDA N-methyl-D-aspartate receptor
NST Nucleus of the Solitary Tract
NT Neurotransmitter
OAT Organic Acids Test
PA Propionic Acid
poly I:C Polyinosinic:polycytidylic acid
PCOA Principal coordinates analysis
PDD-NOS Pervasive Developmental Disorder –
Not Otherwise Specified
SH Sincere Health
TeNT Tetanospasmin
TDL The Doctors Laboratory
TNF α Tumor necrosis factor alpha
VPA Valproic acid
WBC White Blood Cells
5-HIAA 5-Hydroxyindoleacetic acid
5-HT Serotonin

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Glossary
Adiposity Index Ratio of Firmicutes to Bacteroides
Autonomic Nervous System Division of the peripheral nervous system
that influences the function of internal
organs, acts largely unconsciously and
regulates the heart rate, digestion,
respiratory rate, pupillary response,
urination, and sexual arousal. This system
is the primary mechanism in control of the
fight-or-flight response.
Bacteroides Bacteroides is a genus of gram negative
anaerobic bacteria that resides in the
human gut flora.
Blood Brain Barrier The blood–brain barrier is a highly
selective permeability barrier that
separates the circulating blood from the
brain extracellular fluid in the central
nervous system.
Brain-derived neurotrophic factor BDNF is a protein that is part of the family
of growth factors. It acts in the central
nervous system to support the survival or
neurons and the growth of new neurons.
Cecal The large pouch at the beginning of the
large intestine, located in the lower right-
hand side of the abdomen
Central Nervous System The central nervous system is the part of
the nervous system consisting of the brain
and spinal cord. The central nervous
system integrates information it receives
from, and coordinates and influences the
activity of, all parts of the body.
Clostridia Clostridia is a gram positive type of

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Firmicutes.
COX-2 COX 2 is an enzyme that is responsible
for formation of prostanoids, including
prostaglandins, prostacyclin and
thromboxane.
Disaccharidase Disaccharidases are, enzymes that break
down certain types of sugars called
disaccharides into simpler sugars called
monosaccharides.
Dizygotic Derived from two separately fertilized
eggs
Dysbiosis Microbial imbalance in the digestive tract
Enteric Nervous System The enteric nervous system is one of the
main divisions of the nervous system and
consists of a mesh-like system of neurons
that governs the function of the
gastrointestinal system. It has its own
independent reflex activity.
Encephalopathy Encephalopathy is a disorder or disease
of the brain. This syndrome can have
many different organic and inorganic
causes.
Endotoxin Endotoxins are toxic substances bound to
the bacterial cell wall and released when
the bacterium ruptures or disintegrates.
They consist of lipopolysaccharide and
lipoprotein complexes.
Eosinophil Eosinophils are white blood cells and one
of the immune system components
responsible for combating multicellular
parasites and certain infections in
vertebrates. They also control

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mechanisms associated with allergy and
asthma. They develop in the bone marrow
before migrating into blood.
Epithelium Epithelial tissues line the cavities and
surfaces of structures throughout the
body. Many glands are made up of
epithelial cells. Functions of epithelial
cells include secretion, selective
absorption, protection, transcellular
transport and detection of sensation.
Esophagogastroduodenoscopy Esophagogastroduodenoscopy is a
diagnostic endoscopic procedure that
visualizes the upper part of the
gastrointestinal tract up to the duodenum.
Firmicutes Firmicutes are a phylum of bacteria, most
of which have Gram-positive cell wall
structure
Fluorescein isothiocyanate Dextran Substance used in vesicle permeability
studies
Glial Cell Glial cells are non-neuronal cells that
maintain homeostasis, form myelin, and
provide support and protection for
neurons in the brain and peripheral
nervous system.
Hepatic Encephalopathy Hepatic encephalopathy is the loss of
brain function that occurs when the liver is
unable to remove toxins from the blood.
High Mobility Group Protein 1 HMGB1 is secreted by immune cells.
Activated macrophages and monocytes
secrete HMGB1 as a cytokine mediator of
Inflammation.
Hypothalamic–pituitary–adrenal axis The HPA axis is a complex set of direct
influences and feedback interactions

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among three endocrine glands: the
hypothalamus, the pituitary gland and the
adrenal glands. It controls reactions to
stress and regulates many body
processes, including digestion, the
immune system, mood and emotions,
sexuality, and energy storage and
expenditure.
Ileal The terminal portion of the small intestine
extending from the jejunum to the cecum.
Interferon gamma IFNγ is a cytokine that is critical for innate
and adaptive immunity against viral and
some bacterial infections. IFNγ is an
important activator of macrophages. IFNγ
is produced predominantly by natural
killer and natural killer T cells.
Interleukin 1 The Interleukin 1 is a group of 11
cytokines, which plays a central role in the
regulation of immune and inflammatory
responses to infections or sterile insults.
Interleukin 1 Beta IL-1β is a member of the interleukin 1
family of cytokines. This cytokine is
produced by activated macrophages. It is
an important mediator of the inflammatory
response, and is involved in a variety of
cellular activities, including cell
proliferation, differentiation, and
apoptosis.
Interleukin 4 The interleukin 4 is a cytokine that
induces differentiation of naive helper T
cells to Th2 cells. Upon activation by IL-4,
Th2 cells subsequently produce additional
IL-4 in a positive feedback loop.
Interleukin 6 Interleukin 6 is an interleukin that acts as
both a pro-inflammatory cytokine and an

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anti-inflammatory myokine.
Interleukin 12 Interleukin 12 is an interleukin that is
naturally produced by dendritic cells,
macrophages and human B-cells in
response to antigenic stimulation.
Lactase Lactase is an enzyme which breaks down
lactose (found in milk).
Lipopolysaccharide Lipopolysaccharides also known as
endotoxins, are large molecules
consisting of a lipid and a polysaccharide.
They are found in the outer membrane of
Gram-negative bacteria, and elicit strong
immune responses in animals.
Lymphocyte A lymphocyte is any of three subtypes of
white blood cell the immune system. They
include natural killer cells, and B cells.
They are the main type of cell found in
lymph.
Microbiome (Microbiota) The community of commensal, symbiotic
and pathogenic microorganisms that
inhabit the body.
mRNA Messenger RNA is a large family of RNA
molecules that convey genetic information
from DNA to the ribosome, where they
specify the amino acid sequence of the
protein products of gene expression.
Monozygotic Derived from a single fertilized ovum or
embryonic cell mass.
Neuroinflammation Neuroinflammation is inflammation of the
nervous tissue. It may be initiated in
response to a variety of cues, including
infection, traumatic brain injury, toxic
metabolites, or autoimmunity.
N-methyl-D-aspartate receptor The N-methyl-D-aspartate receptor is a

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glutamate receptor and ion channel
protein found in nerve cells. When
activated it allows positively charged
ions to flow through the cell membrane.
It is very important for controlling
synaptic plasticity and memory function.
Nucleus of the Solitary Tract The nucleus of the solitary tract is a
series of nuclei forming a vertical
column of grey matter embedded in the
medulla oblongata, forming circuits that
contribute to autonomic regulation.
Organic Acids Test Organic acids are metabolic byproducts
of cellular metabolism and they can be
measured from a urine sample. It is a
urine test that also that provides an
accurate evaluation of intestinal yeast
and bacteria.
Polyinosinic:polycytidylic acid Polyinosinic:polycytidylic acid is an
immunostimulant. It is used in the form
of its sodium salt to simulate viral
infections
Principal coordinates analysis Principal coordinates analysis is an
ordination technique that is similar to
Principal Components Analysis. The
technique has the advantage over PCA
that any ecological distance can be
investigated.
Tetanospasmin (TeNT) Tetanus toxin is an extremely potent
neurotoxin produced Clostridium tetani
12 Tumor Necrosis Factor Alpha is a cell
signaling protein involved in systemic
inflammation and helps to make up the

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acute phase reaction. It is produced
mainly by activated macrophages. The
primary role is the regulation of immune
cells.
White Blood Cells White blood cells are the cells of the
immune system that are involved in
protecting the body against both
infectious disease and foreign invaders.
All are produced and derived from the
bone marrow.
5-Hydroxyindoleacetic acid 5-Hydroxyindoleacetic acid is the main
metabolite of serotonin. In chemical
analysis of urine samples, 5-HIAA is
used to determine serotonin levels in
the body.

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Introduction
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) of
unknown aetiology; it affects social interaction, communication, interests and
behaviour. ASD effects those in childhood all the way through to adulthood.
Problems include understanding and being aware of others emotions, repetitive
movements and routines (NHS, 2013). An editorial from the medical journal of
Australia highlights the notion that there may be a variety of different conditions that
we group under the term ASD and refers to “autism” as “the autisms”. Whilst these
disorders appear similar in terms of marked behaviors it is likely that “the autisms”
have “different biological underpinnings” (Whitehouse & Stanley, 2013). This is
supported by the high level of heterogeneity in ASD that far exceeds that of any
other disorder (Whitehouse & Stanley, 2013). Taking this into consideration strict
autism (AU) will be separated from ASD in the below review and study.
As of 2010 it is estimated that 1 in every 68 people is diagnosed with ASD. This is
nearly double of that in 2000 (where it was 1 in every 150) highlighting the rapid rise
in prevalence of this disorder (CDC, 2015). These escalating rates give support to an
environmental theory of ASD. Previously ASD was thought of as a genetic condition
however more recent research is dismissive of a solely genetic model. One of the
first truly well powered twin studies was by Hallmayer et al. (2011). This
observational study was appropriately designed in that it accounted for both strict AU
and ASD. The probandwise concordance for monozygotic (MZ) male twins was 58%
(95% CI, 42-74%) for AU and 77% for ASD (95% CI, 65–86%), with 21% (95% CI, 9-
43%) and 31% for dizygotic (DZ) twins (95% CI, 16–46%), respectively. For MZ pairs
in females it was 60% for AU (95% CI, 28-90%) and 50% for ASD (95% CI, 16–
84%). For DZ females the rates were 27% (95% CI, 9-69%)and 36% (95% CI, 11–
60%). Shared environmental factors explained 55% of the variance in AU and 58%
in ASD. The study concluded that environmental factors have a substantial
component whereas the effects of genetics are moderate. Nowadays AU is seen as
a behavioural syndrome that is influenced by both genes and the interactions of
genes and the environment (Herbert, 2005).
The variable insult hypothesis as put forward by Goyal & Miyan (2014) suggests
there may be an environmental insult that disrupts a critical window in development.
The insult takes place from in utero onwards and the theory accounts for a genetic
predisposition. Insults in utero may result in neural crest and/or neural tube defects.
This can result in both structural and functional abnormalities to the peripheral
nervous or immune system which then effects neurological, immunological or
neuroimmunological development. Depending on the timing of the environmental
insult, this affects the associated co-morbidities the child suffers. Examples of this
can be seen in Figure 1.

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Figure 1. Variable Insult Model
G
Goyal & Miyan, 2014, p. 22
ASD is now considered a whole body disorder rather than as solely neurological. It is
now well recognized that those who suffer with ASD are more likely to have co-
morbidities which include; ear infections, allergies, allergic rhinitis, atopic dermatitis,
type 1 diabetes, asthma, gastrointestinal (GI) complaints, sleep disorder,
schizophrenia, headaches, migraines, seizures and muscular dystrophy (Treating
Autism, 2014).
Of all the co morbidities, GI complaints feature both in the clinic and in the literature
considerably. An evidence-based review by Buie et al (2010) concluded that problem
behaviour in ASD might be a direct result of underlying GI disorders. GI disorders
have been shown to be significantly more prevalent in AU than controls (p<0.05;
Parracho et al., 2005). These include reflux, chronic gastritis, constipation, reduced
carbohydrate enzyme activity and chronic diarrhea; many of these are of an immune
disposition and include altered mucosal immunity (Herbert, 2005). Herbert (2005)
suggests a potential self-amplifying feedback loop where intestinal malabsorption
contributes to low nutrient status, which in turn exacerbates the gut disease.
Stomach acid is an important defense mechanism against parasites and bacteria as
the low pH of the stomach acid kill the, upon contact. Chronic hypochloridia
increases the risk of infection by these and reduces mineral absorption at the same
time. This is particularly relevant in the case of zinc due to its crucial role in the
immune system and clinically it has been observed that the patients at Sincere
Health (SH) are low in trace minerals including zinc.

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As mentioned above frequent co morbidities and immune dysfunction is also readily
observed in ASD. Interestingly zinc plays a crucial role in immunity as well as GI
health. In addition to this neuroinflammation is now thought to play a role in the
pathogenesis of AU. Normally the blood brain barrier (BBB) would protect the brain
from the inflammation of the blood and a hyper immune system would not affect the
brain. However activated microglia have been found in deceased AU brains (Vargas
et al., 2005). It may be that there is a link between the increased intestinal
permeability in AU and an increased BBB permeability.
Based upon this theory and rationale a mechanism review will be conducted to
explore the validity of these. In conjunction a retrospective observational study on
the blood and urinary markers of current ASD patients at SH clinic to explore these
relationships clinically.
Objectives:
To conduct a literature review critically analyzing the current research looking at:
• The role of GI disorders in ASD and the implication on mineral status
specifically zinc.
• The role of gut dysbiosis on neurotransmitter function
• The link between GI disorders and impaired immune dysfunction.
• Specific links between GI disorders, immune dysfunction and zinc.
To conduct a retrospective case control study reporting on:
• The prevalence of GI impairment in ASD when compared with the population.
• The comparison of urinary acid markers compared with the population.
• The correlation between GI dysfunction, elevated immune markers and Zinc
status.
CHAPTER 1: Literature Review
1.1 Methodology
Pubmed was the main database used for searching the literature. Each search term
(as seen in table 1) was put into Pubmed. The advanced search tool was used and
the search was limited “title/abstract’ only. The search was limited to the last 5 years.
The yielded search was exported into excel. Each paper was color-coded. Red was
given to papers that were inappropriate based on abstract review. Those that were
relevant but inaccessible were coded yellow. Those that were accessible and
relevant were coded green. Repeat papers were coded blue. A second wave of
literature came from references from initial search papers as well as
recommendations from Pubmed. In total over 700 papers were produced from the
search. SIGN50 and ARRIVE documents were not used to assess the literature due
to time constraints. However papers were included and/or excluded on the following
criteria:
Inclusions
• Papers were included if they were regarding Bacteroides, Firmicutes, Clostridia,
or related strains.

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• Children and young adults (up to 21 years) were included.
• DSM diagnosis of AU was preferred, however studies using other stratified
diagnosis were assessed for reliability and included.
• Papers assessing children with ASD were only used if used if no paper on AU
was accessible
• Studies on non-human participants were included to support biological
mechanisms.
• Studies looking at severe AU and regressive cases have been included.
• All types of studies were included except case reports.
Exclusions
• Case reports were excluded.
• Papers looking at the broader spectrum such as Asperger’s syndrome were
excluded.
• Papers exploring maternal immune activation or GI complaints were excluded.
Search terms
Table 1: Search Terms and Filters used for Literature review
Search Term Number Final Number
Exploring AI & Bacterial Dysbiosis in
ASD
Autis* AND BACT* AND Firmicutes 8
ASD AND Bact* AND Firmicutes 4
Dysbiosis AND Autis* 24
Dysbiosis AND ASD 11
Autis* AND Gut 162
Exploring the Relationships between
Gut bacteria & Neurotransmitters
Bacteroid* AND Firmicutes AND Autis*
AND Serotonin
1
Dysbiosis AND Serotonin 1
Clostridia AND Autis* 29
Serotonin and Autis* 874 117
Autis* AND Gut AND Brain 85
Bacterial Dysbiosis, AI & Immune
System Activation
Dysbiosis AND Immune AND AUTIS* 13
Dysbiosis AND Immune 322 117
Bacteroid* AND Firmicutes and Immune 18
Gut AND Brain AND Immune 427 191
For spreadsheets regarding each search term see appendix 1.

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Findings
1.2 The Relationship between Gut Bacteria and Autism
There are four mechanisms by which the GI microbiome can contribute towards
ASD:
1. Direct Neurological Stimulation – Pathogenic gut bacteria stimulates the
Autonomic Nervous System (ANS).
2. Bacterial toxins cross the Blood Brain Barrier (BBB) causing
encephalopathy. Patients suffering from hepatic encephalopathy are treated
with antibiotics and autistic like symptoms diminish.
3. The gut bacteria themselves produce neurotoxins.
4. The pathogenic gut bacteria result in neuro-inflammation via immune
system stimulation.
The GI tract homes the largest collection of immune cells in the body as well as 500
million neurons. The Bacteria that inhabit it outnumber our body cells by an
estimated 10 to 1. Therefore it is not surprising that GI health plays a huge role in
illness. Of all the bacterial strains Bacteroides and Firmicutes are two of the best
documented. The ratio between the two strains; known as the Adiposity Index (AI) is
of significance in terms of health as it reflects a) nutrients absorbed and the
fermentation of food, b) immune system activation and c) the barrier against
pathogens. An elevated Firmicutes: Bacteroides ratio is seen in obesity and the
reverse is related to weight loss (Mariat et al., 2009). Finegold et al (2012) found that
those with severe AU had 38% Firmicutes, to 51% Bacteroides, where healthy
controls had 63% Firmicutes and 30% Bacteroides.
Impaired gut health is often characterized by increased intestinal permeability widely
referred to as “Leaky Gut” syndrome. This is where an insult to the tight junctions of
the epithelial cells results in increased intestinal permeability. This increased
permeability allows larger molecules that would not normally pass the barrier, into
the blood stream. This is said to activate the immune system and result in persistent
low-grade inflammation. It appears that approximately 43% of those with ASD also
suffer with leaky gut (McElhanon, Et al., 2014).
The largest current population based case control study investigating GI complaints
in ASD took data from the CHARGE (Childhood Autism Risks from Genetics and
Environment) study of 960 children. Children aged between 2 and 5 years with GI
complaints were extracted. It found that children with ASD were three times more
likely to experience GI symptoms than children of typical development. Within
children with ASD those with GI symptoms rated significantly higher on behavioural
symptoms such as irritability, social withdrawal, stereotypy and hyperactivity
(p<0.001). This study was well designed in that it originally separated AU from ASD;
however no difference was found in GI symptoms between the two groups except
diarrhea (Chaidez et al., 2014). This study also accounted for the confounding effect
of medications on GI symptoms. The study unlike a lot of literature in this area has a
large sample size.
A case control study of 30 children (4-10years) explored AI in those with either

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Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) or AU. They
found that when compared to PDD-NOS and healthy controls (siblings) both the total
and active amount of Firmicutes was significantly lower in AU children but the
amount of Bacteroidetes was significantly higher (p<0.05; De Angelis et al. 2013).
Interestingly there was no significant difference between PDD-NOS and controls.
PDD-NOS is often grouped under the autism spectrum, supporting the previous
argument that Autism should be separated from the spectrum. Those with PDD-NOS
show atypical autistic symptoms, the difference in gut microbiota between AU and
PDD-NOS may provide a partial explanation for this. A limitation is that healthy
controls were siblings and therefore not age matched. However the study compared
not only the differences in the bacterial composition but also in the activity. The
metabolic activity is of real importance as few studies have looked at this. This
comparison in siblings may be of importance when deciphering between host
genetics and environment. As with all research in this field, the sample size is small.
Bacteroides has also been found to be lower in ileal and cecal biopsies in AU
children (mean age 13.4 months) when compared with controls (p=0.012, r=0.31,
William et al, 2011). This case control study was well designed in that it compared
the biopsies of AU children with GI complaints (AU-GI) with controls with GI
complaints (control-GI). Therefore hopefully allowing us to control for the co-variance
produced by GI complaints as a whole. The Firmicutes/Bacteroides ratio was
significantly elevated in the AU-GI group (p=0.007, r=0.45) although there was no
significant different in Firmicutes alone. The mRNA transcription factors were
significantly lower in AU-GI patients (p<0.001) and 80% of the AU-GI had activity
below the 25th
percentile of the control-GI patients. The lack of disaccharidases will
affect carbohydrate digestion and the unabsorbed carbohydrates will likely result in
osmotic diarrhea as well as gas and bloating (as seen in AU). This is of particular
importance as the only significant difference between ASD and AU according to the
CHARGE study is diarrhea. Therefore the authors concluded it was the genetic
deficiencies in mRNA of disaccharidases that results in an environment that favours
pathogenic bacterial overgrowth. They acknowledge that whilst diet can regulate
mRNA activity no studies have of yet found higher carbohydrate consumption in
ASD. The study was all male and had a very small sample size (n=22). However
due to the laparoscopic nature of the study, a small sample size is understood. It
may therefore be what makes a child vulnerable to strict AU as opposed to ASD is
the inherent differences in mRNA of disaccharidase enzymes.
Further work to support the DNA modulating nature of Bacteroides was explored in
vitro. Krinos et al (2001) found that b.fragilis was able to interact with its host
organism by modulating the expression of polysaccharides. It was able to reversibly
invert the expression in an “on” “off” manner and may therefore explain the diversity
of the behaviour of the species and an inconsistency in findings. Whilst De Angelis
et al (2013) and Williams et al. (2011) found opposing results in the levels of
Bacteroides and Firmicutes, it is worth noting the drastic age difference between
participants. AI has been noted to increase from 0.4 (infants) to 10.9 (adults) and
drop to 0.6 (elderly) throughout the lifetime (Mariat et al. 2009). Highlighting the need
to appreciate the age difference in the diversities of species in the gut microbiome
and more importantly the role they may have at different ages.

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Kushak et al. (2011) also looked at the effect of age on intestinal disaccharidase
activity in 199 ASD children (median age= 5.75yrs). It was a retrospective cohort
study on children who had undergone esophagogastroduodenoscopy. As expected
they found that lactase activity was significantly lower in children over 5yrs (p=0.02).
AU children <5 were 58% deficient whereas those over 5 were 65%. Males < 5 had a
1.7 fold lower lactase activity than females (p=0.02) whereas those >5 had a 2.2 fold
lower activity rate (p=-.006). Lactase activity was significantly affected by mucosal
inflammation (4.61+/- 0.75U/g in inflammation vs. 15.34 +/- 99U/g without
inflammation, p=0.03). However this study is severely limited in the lack of
heterogeneity of the autistic population, it included IBD, downs syndrome and ASD.
It may be therefore that the inflamed mucosa was specific to the IBD patients.
Retrospective studies are prone to investigator and confounding sources of bias.
A mouse model whereby the mothers were immune compromised by viral mimic
poly(I:C ) to produce offspring that resemble autistic features was studied by Hsiao
et al. (2013). The mice were born with intestinal permeability (measured by
translocation of FITC-dextran across the intestinal epithelium, p<0.01). PCOA
indicated a significant taxonomy of bacteria in ASD rats (p=0.07, R=1.051) and
90.1% of the ASD rats were contaminated with classes of Clostridia and
Bacteroides. They found that treatment of the Bacteroides. Fragilis with probiotics
corrected the tight junctions in the colon but not the small intestine (Bacteroides are
predominantly found in the colon) and improved the ASD behaviours. Impaired
disaccharidase digestion could result in b.fragilis overgrowth as it has been shown to
use utilize a wide range of dietary polysaccharides (Wexler 2009). In addition to this
they also note that the histolytic enzymes found in b.fragilis can result mediate tissue
destruction as well as activating macrophages with decreased Nitric Oxide
production and thus evading their own death.
Firmicutes can be divided into its anaerobic subgroups, one of which is Clostridia.
Significantly higher levels of Clostridia are also seen within ASD when compared to
healthy controls (P<0.01, Parracho et al., 2005). This was found in a small (n=58),
predominately male case control study of ASD patients, healthy siblings and
unrelated healthy children aged 3-16years. Those with ASD were significantly more
likely to have GI complaints (p<0.05) than controls. 91.4% of ASD had GI complaints
compared with 25% of siblings and 0% of unrelated controls. In ASD the most
common GI symptom was diarrhea (75.6%). This research ties in with the above
study by Chaidez et al., (2014) where the only marked difference between AU and
ASD was the presence of diarrhea. It may therefore that AU research should be
concentrating on the gut bugs pertinent to diarrhea. GI symptoms were positively
associated with clostridia (p<0.001, Parracho et al., 2005) however there was no
difference in clostridia between ASD patients and siblings. The authors note that this
highlights the effect of environmental factors and host genetics on bacterial species.
However 25% of the siblings had GI complaints, therefore as the sample size was
small there may not have been sufficient power to look for differences between the
two groups. The authors note that clostridia are recognized neuro toxins and
therefore the overexpression of these may be why parents with worsening with GI
complaints reported behaviour.

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A systematic review on the gut microbiome in ASD (Cao, X et al., 2013) analysed 11
papers al of which had relatively small sample sizes (50% of papers had n<50). The
authors concluded that no conclusion could be made on the specific bacterium and
ASD, due to conflicting data regarding Bacteroides, Firmicutes and Proteobacteria.
However the authors note this could be due to different subgroups within ASD. The
lack of available studies for the review highlights how understudied the area is, and
that the available data has poor methodology. It could be that the presence of
subgroups such as Clostridia is what affects the AI ratio. Pathogenic gut bacteria
compete with each other and this competition may be effected by host genetics
however the result on AU symptomatology remains similar. The lack of
disaccharidase enzymes has been a constant argument for ASD, and is often
supported by the opioid theory of Autism (Panskepp, 1979). This theory suggests
that compromised breakdown of gluten and casein (by damaged or insufficient
disaccharidases), results in these opioid peptides passing through the impaired
intestinal border and binding to opioid receptors in the brain. It may be that this
reversible activity of gut bacteria to modulate mRNA and DNA expression is where
research should be focusing. This may set the dynamic of the gut environment,
which then leaves the GI tract vulnerable to other pathogenic strains.
1.3 The Relationship between Gut bacteria and Neurotransmitters
To date the gut microbiome has been acknowledged to effect anxiety like behaviors,
depressive like symptomatology, nociceptive responses, stress responsiveness,
feeding behaviors, taste preference and metabolism in rats; administration of
probiotics reversed these behaviors (Mayer et al., 2015).
The most well known documented effect of pathogenic gut bacteria is the ability of
clostridia particularly clostridium tetani to produce the tetanus neurotoxin (TeNT;
Bolte, 1998). An older but particularly well-designed in vitro study by Elsden et al.
(1976) showed the ability of Clostridia to catabolize aromatic acids. The vagus nerve
provides a transport mechanism for the neurotoxin from the intestine to the CNS,
once in the brain it disrupts the release of neurotransmitters by the proteolytic
cleavage of synaptic vesicle membrane proteins (Bolte, 1998).
Following on from the work of Bolte (1998) Song et al (2004) found that C.bolteae
and clostridia cluster’s I and XI were 46 fold (p<0.01), 9 fold (p=0.0014) and 3.5 fold
(p=0.004) respectively, greater in AU stool than controls. Finegold (2011)
investigated clostridial spores in a pilot study. By doing a PCR real time stool
analysis they found that Desulfovibrio rather than Clostridia that was more prevalent
in AU than controls. However the authors note that this does not diminish the role of
clostridia in AU and believe that antimicrobial intervention studies on both bacteria
are desperately needed.
Desulfovibrio produces lipopolysaccharide (LPS; endotoxin produced by gram
negative bacteria), which specifically depletes the body of sulphur. Some sulfate-
reducing bacteria can carry out propionic acid (PA) fermentation. McFabe et al.,
(2010) administered rats with PA (metabolic end product of gut bacteria). They found
that this increased restrictive and repetitive behaviors (as determined by an object
choice test), impaired social behaviour and impaired reversal learning. Brain tissue
analysis revealed activation of the microglia indicating neuroinflammation. PA can

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increase NMDA receptor activity as well as promoting intracellular calcium release
and elevating nitric oxide, which can affect neuronal synaptic transmission. A case
control study of 232 AU children by Waring & Klovrza (2000) showed that they had
increased urinary sulphite (106.9 +/- 162.9, p<0.001) and urinary sulphate (6819 +/-
6712.3, p<0.001) excretion versus controls. Increased urinary excretion is suggestive
of decreased plasma sulphate. Supporting the argument for sulphur reducing
bacteria in AU as opposed to Clostridia. The gut lining is compromised of sulphated
glycoprotein, therefore not only can the PA have a direct effect on brain tissue in can
also result in further pathogenesis by disrupting gut function and increasing
permeability (Murch et al., 1993).
A systems-based computational model of the gut microbiome and regressive AU
was put forward by Downs et al. (2014). They found that in rats PA resulted in
autistics symptoms. Bacteroides vulgaris is known to increase the levels of PA. The
computation was based upon the proposed mechanism as seen in Figure 2.
Figure 2. Systems-Based Computation Model of the Gut Microbiome and
Regressive Autism
Downs et al. (2014) p. 650
It can be seen that the virtulence factor produced by the b.vulgaris results in
increased cytokine production as a result of the immune response to their presence.

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The cytokines increase the gut permeability, which further mediates cytokine
production. The cytokines eventually cross the BBB, where they can induce neuro
inflammation. Supporting this, increased cytokine levels including TNFα and IFNγ
have been observed in the bloods and brains of ASD children (Xu et al., 2015). The
model shows that brain permeability increases as a result of cytokine levels. It is
possible for PA to cross the BBB regardless of cytokine concentration although the
presence of these will increase the rate of transfer.
This model is innovative and allows the comparison of the rates at which different
processes occur between AU and healthy individuals. However whilst sophisticated
as the model is, it has simplified bodily processes and it is not clear to what extent.
This is an inherent problem with modeling. Whilst it showed that PA resulted in
autistic symptoms, disease in humans is multisystemic and basic causality cannot be
assumed. The model is promising in that it highlights the limited rate of clearance of
cytokines from the brain and recommends a long-term treatment plan, partly due to
the ever-changing nature of the microbiome. This may also explain as to why short-
term treatment, particularly of clostridia results in a re-emergence of symptoms when
treatment stops (Sandler et al., 2000).
A mouse model of ASD by De Theije et al. (2014) found that the AI was significantly
elevated (76.4% Firmicutes, 19.7% Bacteroides). Interestingly 73% of the Firmicutes
was from the Clostridia strain, which was correlated with ileal serotonin levels
(r=0.509, p<0.05). These GI compositional disturbances resulted in intestinal
inflammation, which had an effect on the social behaviour of the mice. However it is
not possible to generalise from mouse models to humans. As with all animal studies
the major critique is not only the lack of generalizability but also how does one differ
between different mental disorders in mice. The authors used valproic acid (VPA) to
induce ASD in mice; the VPA induces lesions in the brain stem and damages
neurons. It therefore could be that it is the nerves that are in control of deciding what
bacteria should and shouldn’t colonate the GI tract and it may be that this
mechanism is impaired in ASD (Goyal, 2015). Interestingly TeNT inhibits the sodium
dependent serotonin uptake at the terminal (Humeau et al., 2000) and this could be
a mechanism by which increased ileal serotonin is found, although gut bacteria are
capable of producing neurotransmitters.
A study on mice by Bercick et al (2011) had opposing findings. They found that
whilst the microbiota in the gut influenced brain behaviour and chemistry this was
independent of GI specific NTs such as Serotonin, the ANS and inflammation. They
found no significant changes between microbiota and TNFα, IL-1β, IL-4, IL-6, IL-12
and IFNγ in tissue samples between control and mice that had had their microbiota
perturbed by antimicrobial (ATM) administration. A week after administration brain
derived neurotropic factor (BDNF) levels was significantly lower in ATM mice
(p<0.01). However they did find that after 3 weeks of administration, BDNF levels in
the amygdala and hippocampus were no different with controls. Suggesting that
these mechanisms are involved at the induction but not maintenance of altered
behaviour. However whilst the mice in this study showed altered behaviour, the
previous study had used a more appropriate model of AU induction, and it may be
that the difference in brain pathologies between the two highlight the mechanism to
be explored separating AU from other psychiatric behaviors.

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It is hypothesised that neurotoxins produced by gut bacteria can result in
encephalopathy and the ability of Clostridia tetani to produce TeNT supports this.
Encephalopathy can have a variety of causes including viral, autoimmune, and
hepatic. The treatment of Hepatic encephalitis supports this mechanism. Hepatic
encephalitis is treated by antibiotic administration, as they kill the bacteria that are
producing ammonia based by products, and this leads to a reduction of symptoms.
Whilst not formally included in the search results it is worth noting that a case report
by Creten et al. (2011) hypothesized that anti-NDMA-receptor encephalitis might be
the cause of some autisms particularly regressive cases due to the ability of NMDA
to regress autistic symptoms. They treated a 9yr old male with late onset AU with
antibody treatment for the anti-NDMA-receptor encephalitis, and saw a regression in
AU symptoms and a return to normal life. However this was just one case report and
it could be that whilst it was the organic cause in this case it is not always. It does
highlight the effect of toxicity on behaviour. Previously it has been mentioned that
PA produced as a result of gut bacteria can also heighten NMDA activity (McFabe et
al., 2010). Further research on this mechanism is lacking and much needed.
As well as neurotoxins being produced by GI bacteria it is also possible that they
may produce neurotransmitters (NT) themselves. The strains Enterococcus spp. and
Escherichia spp. both produce serotonin (Cryan & Dinan, 2010). These produced
NTs then induce the epithelial cells to release molecules that modulate neuronal
signalling with the Enteric Nervous System (Forsythe and Kunze (2013). The above
murine model (de Theije et al. 2014) found that there was increased ileal serotonin
but decreased overall serotonin levels (p<0.05). Excess serotonin can result in
diarrhoea and poor co ordination. Increased ileal serotonin could explain why there is
increased communication between the gut and the CNS. It has been consistently
found that 25% of AU children are hyperserotonemic, however stabilization is usually
seen by age 9 (Cook., 1990). Although high serotonin levels do not necessary mean
higher signaling, as it is also dependent on serotonin transmitter receptors. It also
may be that the excess ileal serotonin operates a negative feedback mechanism
resulting in lower serotonin levels elsewhere.
A murine model by Clarke et al. (2013) addressed the effects of the gut microbiota
on the CNS particularly via the serotonergic system in the hippocampus. In germ
free (GF) they found that tryptophan (precursor of serotonin) was significantly
increased (p<0.05). The authors suggested a humoral route that the microbiota may
influence the CNS. Fascinatingly colonization restored peripheral tryptophan to the
levels of the control mice but had no effect on 5-TT and 5-HIAA which remained
significantly higher whilst BNDF remained significantly lower (p<0.05). These mice
continued to show increased anxiety post colonization. This was only found in male
mice however the authors note that sex differences should be applied to humans
with caution. The problem with using GF mice is that whilst they are lacking
“beneficial” bacteria it does not take into account of any endotoxic effects of
pathogenic gut bacteria.

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1.4 The relationship between Gut bacteria, Neuroinflammation and Immune
Activation
“Microbiota can have a direct effect on the immune system, the innate and adaptive
immune system collaborate to maintain homeostasis at the luminal surface of the
intestinal host microbial interface which is crucial for maintaining health. The immune
system also exerts a bi directional communication with the CNS, making it a prime
target for transducing the effects of bacteria on the CNS. In addition, indirect effects
of the gut microbiota and probiotics on the innate immune system can result in
alterations in the circulating levels of pro-inflammatory and anti-inflammatory
cytokines that directly affect brain function” Cryan & Dinan., (2012) p. 704.
Table 2. Taxonomic Examples of Bacteria from the Intestine
Phylum Class Species Contributions to Host Physiology
Bacteroidetes Bacteroidales
Bacteroides
thetaiotaomicron
Complex polysaccharide hydrolysis (Martens et al., 2008
and Sonnenburg et al., 2005)
Bacteroides
fragilis
Immune modulation by capsular polysaccharide
biosynthesis (Coyne et al., 2005, Liu et al., 2008,
Mazmanian et al., 2005 and Mazmanian et al., 2008)
Bacteroides
ovatus
Plant polysaccharide hydrolysis (Hespell and Whitehead,
1990)
Firmicutes Bacilli
Lactobacillus
plantarum
Inhibition of intestinal inflammation, probiotic (Petrof et al.,
2009)
Lactobacillus
brevis
Attachment to the Intestinal epithelium, probiotic (Avall-
Jaaskelainen et al.,2003)
Lactobacillus
acidophilus
immune modulation, induction of intraepithelial lymphocyte
expansion (Roselli et al.,2009)
Lactococcus
lactis Potential probiotic (Avall-Jaaskelainen et al.,2003)
Enterococcus
faecalis
Immune modulation, interleukin-10 stimulation, biogenic
amine synthesis, horizontal gene transfer (Are et al.,2008,
Ladero et al. 2009 and Salyers et al., 2004)
Enterococcus
faecium
Biogenic amine synthesis, horizontal gene transfer (Ladero
et al., 2009 and Salyers et al., 2004)
Clostridia Clostridium spp.
Butyrate metabolism, associated with inflammatory bowel
disease (Gophna et al., 2006 and Manichanh et al., 2006)
Actinobacteri
a Actinobacteria
Bifidobacterium
longum
Immune modulation, intraepithelial lymphocyte expansion
(Roselli et al.,2009)
Proteobacteri
a
Î³-
Proteobacteria
Enterobacter
cloacae Immune modulation (Macpherson et al., 2000)
Duerkop et al., (2009), p. 369.
The relationship between gut bacteria and the immune system is complex. The table
highlights how different bacterium contributes to host immunity in both in vivo and in
vitro studies (Duerkop et al., 2009). Whilst it is beyond the scope of the paper to
analyse every mechanism, it is worth acknowledging how different strains can
modulate immunity and how well recognised it is becoming. The first mechanism by
which gut bacteria can affect immunity is via direct stimulation of the ANS.
Stimulation of the ANS is the main way between which the CNS and ENS
communicate. Vagal and sensory neurons terminate at different points throughout

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the epithelium in which play an important role in the transfer of information. The
communication between the two is bidirectional and emotion and stress-based
disorders have long been associated with dysbiosis (de Jonge.,2013).
A murine study by Goehler et al (2008) challenged mice with Campylobacter jejuni.
They found the mice showed anxiety like behaviour as measured by reduced
exploration of open arms in a maze and that brain regions associated with autonomic
function were activated. There was significant expression in the Nucleus of the
solitary tract (NST) in the treated mice when compared with controls (p<0.0003).
The NST is crucial for receiving input from the vagus nerve and relaying it to the rest
of the brain, this suggests that the vagus nerve is the likely mechanism by which
pathogenic bacteria alter behaviour. In addition to this H.pylori has been shown to
affect gastric neural circuitry. A mouse model of H.Pylori by Bercik et al. (2002)
found increased muscle stimulation as a result of increased density of nerves in the
epithelium when compared with controls (p=0.04) suggesting the direct of effect of
microbes in stimulating ANS activity.
The other mechanism by which gut bacteria can interact with immunity is via
neuroinflammation. Typically the CNS is protected from inflammation in the body as
the BBB protects the brain from inflammatory cytokines in the blood, which are
unable to permeate the barrier. However if this barrier becomes compromised,
microglia become activated and perpetuate the immune response. Whilst initially this
is to protect the brain, microglial activation can lead to neuronal damage and death.
Neurotoxins produced by gut bacteria or cytokines produced as a result of dysbiosis
can lead to impaired permeability of the BBB. Cunningham et al (2005) proposed the
“microglial priming hypothesis”; this predicts that microglial are primed by an existing
pathology to aggressive inflammatory responses which results in neuronal death.
They primed mice with tomato lectin and then later challenged them with
Lipopolysaccharide (LPS). They found that those that had been primed produced
significantly more IL-1β, TNFα, IL-6 (p<0.001, p<0.01 and p<0.05 respectively). In
addition to this there was marked up regulation of COX-2 in endothelial cells
suggesting increased communication between the CNS and ENS.
Bacterial dysbiosis can result in increased gut permeability, which leads to immune
activation and low-grade systemic inflammation. A case controlled study by Babinska
et al (2014) looked at the pathogenesis between the two. The study was
predominately male (26 vs. 5) high and low functioning AU patients aged 2-22years
(mean age 9.0±5.6 years) compared with 16 age-matched controls (10 of which
were siblings). They found that plasma HMGB1 levels were significantly higher in
subjects with autism (13.8±11.7 ng/ml) than in the control group (7.9±4.0 ng/ml,
p<0.02). This was not correlated with age (r=0.03; p=0.86). GI complaints (as
measured by questionnaire) were found in 96.8 % of subjects with AU, which was
significantly higher than controls (66.6 %; df=1, p<0.05). AU subjects with higher
plasma HMGB1 levels (11 ng/ml or higher) also had a higher median score of GI
symptoms (8.0, 95% CI 5.8-9.8) The AU subjects with lower HMGB1 levels (<11
ng/ml, n=12), also showed a lower median score of GI complaints (3.0, 95% CI 2.9-
7.2, p<0.04). HMGB1 is secreted by activated macrophages and monocytes, and it
acts to mediate inflammation. Whilst this study shows a strong link between GI
dysfunction and immune system, no causation can be inferred. Interestingly HMGB1

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is released as a response to LPS. This gives support to the earlier hypothesis that
AU is a result of endotoxemia produced by pathogenic gut bacteria. Whilst
promising, the sample size of the study was small. In addition to this no
differentiation was made between high and low functioning AU. It may therefore be
that further work on the severity of AU, and how it relates to GI dysfunction and
immune activation is needed.
Endotoxins may also result in systemic inflammation via activation of the liver
macrophage cells. A murine model by Qin et al. (2007) showed that injection of LPS
caused initial incline of liver and serum TNF α levels. However these results were
short lived and they declined to basal levels after one week and 9 hours respectively
(p<0.05). However it could be argued that in humans with pathogenic microflora the
LPS stimulation would be constant rather than isolated so serum and liver TNF-α
would remain constantly elevated. Remarkably the authors found that one injection
of LPS led to elevated brain levels of TNF α for up to 10 months. Analysis of the
brain showed that it resulted in substantia nigra, Hippocampus and Cortical
activation of microglia (activated macrophages of the CNS). It also resulted in
progressive loss of Dopaminergic neurons as the microglia not only induce neuron
damage but they can become persistently activated in a cycle of neurotoxicity which
ceases to end despite the originating stimulus being dissolved.
A case controlled study (n=28) investigated the relationship between severe AU and
endotoxemia (Emanuele et al. 2010). This study was specialised in that it looked at
nonverbal ASD compared with healthy controls (18 males and 4 females; mean age:
28.1 ± 7.7 years, there was no statistical difference in this between control and
experimental p=0.78). Controls were age and gender matched. As the AU
participants were nonverbal, behaviour was assessed using a behavioural scale
based upon interviews with friends and family. Healthy controls were excluded if
there was a family history of psychiatric illness however there was no mention of
exclusion based upon GI familial illness. Serum endotoxin levels were significantly
higher in the ASD group (p<0.001). They also showed a trend towards higher levels
of immune activity than controls, particularly IL-1, IL-6 however this was non-
significant (this could have been due to insufficient power). A negative association
was found between behaviour and serum endotoxin levels (p<0.001). Whilst the
mechanism suggests that this may occur via an impaired GI permeability, this was
not measured and therefore cannot be concluded. The ASD subjects were recruited
from a farming community whereas the controls were taken from both this
community and laboratory personnel, therefore there may be an environmental
exposure in the ASD group that has not been accounted for.
An intervention trial by Sandler et al (2000) with no case controls administered
vancomycin (500mg/daily) for 8 weeks to 11 regressive AU children (10 males; 43-
82months). They found that during intervention communication and behaviour
significantly improved (as measured by an independent psychologist; Z score = -2.9,
p=0.03). This finding reversed when the children finished the course of the antibiotic.
However neural neuropathies take a long time to heal and it may be that any
damage done via endotoxins need to be given a long time to heal. Qin et al., (2007)
found that inflammation was raised for 10 months following endotoxin exposure;
therefore the treatment intervention in this study may have been too short. Probiotic

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treatment was given following discontinuation of the vancomycin, however the
authors note that compliance was poor. Therefore it is unlikely that there was any
colonization of “beneficial” bacteria in the GI tract.
If gut bacteria is the mechanism by which neuroinflammation occurs it can be
reasoned that administration of probiotics would reduce psychiatric symptomatology.
As mentioned above treatment of mice with B.fragilis removed AU behavioural
symptoms (Hsiao et al., 2013). The authors proposed the mechanism in Figure to
explain their finding.
Figure 3. Mechanism of probiotic treatment in Autism.
Hsiao et al. (2013) p. 1463
Whilst the studies looking at the effect of probiotic treatment in AU is limited, studies
in other brain/gut disorders exist. A mouse model by Bravo et al. (2011)
administration of the probiotic L. rhamnosus reduced depressive like symptomology
(t = 3.926, df = 14; P < 0.01). It also modulated GABA mRNA receptors (p<0.0001),
suggesting the vagus nerve may play a role in this modulation as well as the
potential ability for gut bacteria to effect DNA expression. Similarly B Infantis and L
Helveticus have also been shown to have antidepressant properties (Kennedy et al.,
2014). Ultimately the research in this area is limited coupled with the vast amount of
probiotic strains available.

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Minerals particularly zinc status plays a key role in immunity and ASD. Benjamin
(2014) critically analyzed the role of zinc and concluded that it plays a key structural
role in mucosal integrity as well as for immune cell differentiation. In addition to this it
is needed for the activation and regulation of cytokines. Similarly it activates the HPA
axis, which ultimately leads to the suppression of lymphocyte production.
Particularly reduction of zinc, leads to reduction of stomach acid, which leaves the
stomach vulnerable to infection (Tennant et al., 2008). Appetite suppression is also a
result of zinc, which may explain the picky eating seen in ASD. As a comprehensive
analysis has been provided by Benjamin, (2014) further analysis of zinc is beyond
the scope of this paper however she did conclude that plasma zinc was significantly
reduced in AU children aged 0-6 years.

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Figure 3. Proposed Mechanism Derived from the Literature Review
* Initially we may see an increase in lymphocyte production as the GALT responds to
the increased toxicity. However these are produced in the gut wall and production is
reliant upon sufficient nutrients and gut integrity.
Impaired Disaccharidase Digestion
GI complaints particularly diarrhoea,
gas and bloating
Immune Compromised
Environment favouring sulphate-
reducing bacteria
Production of sulphites
Nutrient Deficiencies/Food
Intolerances
*Reduction in lymphocyte production
– reduction in SIgA & IFNγ
Neurotoxins/ toxins produced Impaired gut integrity/dysbiosis
Reduction of beneficial bacteria
Insufficient mRNA transcription
enzymes
Environment favouring pathogenic
overgrowth particularly Clostridia
(Firmicutes)
Ileal Serotonin production
Prevention of the normal metabolism
and detoxification of NTs
Antibiotics/Prescription medication
Encephalopathy /Autistic Symptoms

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CHAPTER 2: STUDY
A retrospective observational study was conducted using medical records from SH
(a private medical practice specialising in Autism).
Research Hypotheses:
Null Hypotheses:
1. There will be no difference between the AI of AU children when compared
with the general population.
2. There will be no correlation with bacterial organic acid test (OATS) markers
and AI in autistic children. Age will not have an effect.
3. There will be no difference between bacterial and neurotransmitter OATS
markers in AU children when compared with the population mean.
4. There will no relationship between bacterial markers in the OATS and raised
inflammatory markers in AU children.
5. There will be no relationship between zinc, AI and OATS markers in AU
children.
Experimental Hypotheses:
1. The AI of AU children will be significantly elevated when compared with the
general population.
2. There will a correlation with bacterial OATS markers and AI in AU children.
This will be more pronounced at younger ages.
3. Bacterial and Neurotransmitter OATS markers will be significantly different in
AU children when compared with population means.
4. Bacterial Markers in the OATS will correlate with raised inflammatory markers
in AU children.
5. Zinc deficiency will correlated with Adiposity Index and OATs markers in AU
children.
2.1 Methodology
Patients at SH had demographic information including age, sex and diagnosis taken
via questionnaire on admission. Upon examination patients were referred for testing;
this included haematology, Organic Acids, GI Effects - Microbial Ecology Profile (AI)
and hair mineral. As the study was retrospective ethics approval was not needed
however the study was conducted under the supervision of Dr Goyal.
Haematology
Patients attended The Doctors Laboratory, (TDL) in London where blood was drawn
for analysis. After analysis the following blood markers were taken for statistical
investigation:
• Platelets (10^9/L)
• White Blood Cells (10^9/L)
• Lymphocytes (10^9/L)
• Eosinophils (10^9/L)
• ESR (mm/hr)

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Organic Acids
The OAT was administered via Great Plains Laboratory. Patients were required to
provide 10mL of first morning urine into a sterile container. Patients were advised
that urines sample should be taken prior to food or drink and that apples, grapes
(including raisins), pears or cranberries 24 hours prior to collection. If samples were
too diluted (not yellow in colour) patients were advised to discard sample and re
collect. Samples were frozen until able to ship. Samples were shipped along with a
frozen gel pack (provided in testing kit) to Great Plains, USA. For the extensive list of
all items measured please see appendix 2. All items were measured in mmol/mol.
For this study patients’ results for the following were extracted:
• Citramalic Acid
• Hippuric Acid
• Succinic Acid
• Dihydroxyphenylpropionic Acid (DHPPA)
• 3-3-hydroxyphenyl-3-hydroxypropionic Acid (HPHPA)
• 2HydroxyHippuric Acid
• Homovanillic Acid (HVA) & Vanilmandelic Acid (VMA)
• 4-Cresol
• 5-Hydroxyindoleatic Acid (5-HIAA)
• Quinolinic Acid
• Kynurenic Acid (KYNA)
• Quinolinic/5-HIAA Ratio
The above markers were included as they are markers of neurotransmitter
metabolism and markers of GI Dysbiosis with the exception of Succinic Acid (Krebs
cycle metabolite) and 2-HydroxyHippuric Acid (indicator of metabolism). Succinic
Acid and 2-HydroxyHippuric Acid had been noted to be elevated in clinical
observations and therefore were included by the advice of Daniel Goyal at SH.
GI Effects - Microbial Ecology Profile (AI)
The Microbial Ecology Profile is a stool test analysed by Genova diagnostics, Stool
was analysed using DNA by PCR analysis. Stool was collected in a container and
was not to be contaminated with urine or toilet water. A spoon was provided to
transfer sample to specimen tube and filled to line. Samples were to be shaken to
mix with preservative in tube. Samples were refrigerated until ready to ship. Patients
were asked to refrain from taking digestive enzymes, antacids and aspirin two days
prior to specimen collection unless otherwise specified. Those taking antibiotics,
antifungals, probiotics or foods containing beneficial flora were advised to wait a
minimum 14 days before specimen collection. Samples were shipped to Genova
Diagnostics, UK. The Bacteroides and Firmicutes measures were extracted from
reports. Excel was used to create the ratio between the two to provide the AI.
Hair Mineral Analysis
Patients were required to send two tablespoons of hair to Biolab, London. Hair that
had been chemically treated could not be used and 12 weeks should be elapsed
before sampling. Patients were allowed to take nutritional supplements. Provided
hair needed to be taken from as close to the back of the head, nape of the neck or
close to the scalp as possible. Hair analysis was carried out at Biolab. Whilst 18 toxic

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metals were analysed only the zinc measurements were extracted for the purpose of
this study.
The testing laboratories gave age specific reference ranges for both gender and
those above/under 13years. The data was then split into males<13, females<13,
males>13 and females>13. After segregation it was decided that only males<13
would be analysed, as sample sizes in the other groups were not sufficient.
Statistical Analysis
When comparing AI, OAT markers, Immune markers and Zinc with the populations
mean a one-sample T test will be used. For correlational analysis of populations
greater than 30 Pearson’s correlation (parametric) will be looked at. For correlational
analysis of populations less than 30 Spearman’s Rho (non parametric) will be looked
at. Both a MANOVA and a MANCOVA will be looked at to assess multivariate
correlations between AI and OATs markers.
Table 3. Age and Gender Specific Population Means
Measurement Population Mean for Males<13
Adiposity Index 0.4
Hippuric Acid 340
Succinic Acid 11.5
DHPPA 0.295
HPHPA 110
2Hydroxhippuric Acid 0.6
HVA and VMA 1.515
4-Cresol 42
5-Hydrocyindoleactic Acid 5.5
Quinolinic Acid 4.64
KYNA 2.1
Citramalic 1.25
Quinolinic Acid/ 5-HIAA Ratio 2.5
WBC 6.5
Platelets 275
Eosinophils 0.4
Lymphocytes 5
ESR 5.5
Zinc 200
The test results for each patient were manually entered into Excel. Each patient was
given a unique identifier in order to create anonymity. Information regarding gender
and age was kept with the data, as this was crucial to the study. However date of
birth was removed and an age was given, so the records would not be identifiable.
Only patients diagnosed as Autistic or having Neurodevelopmental Disorder (NDD)
were included. AU patient with regressive AU were included. No case controls were
used in the study, AU patients were compared with the population means as seen in
table 3. The benefit of using the population mean for comparison rather than age
matched controls provides by that a laboratory is that it minimises selection bias. By

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definition those who are paying to have medical tests are likely to have a
compromised biochemistry and cannot be assumed to be a health control even if
they are not autistic. AU.
Once all appropriate data was extracted into Excel it was then imported to IBM
SPSS for statistical analysis.
2.2 Results
The two tables below show the descriptive statistics for males under 13. These tests
were run to assess the normality and characteristics of the data.
Table 4. Descriptive Statistics for AU Males < 13
Measurement Mean Standard
Deviation
Normal P -Kolm Normal P -
Shapiro
Adiposity Index 1.921 0.934 <0.00 <0.00
Hippuric Acid 308.421 425.556 <0.00 <0.00
Succinic Acid 24.779 22.617 <0.00 <0.00
DHPPA 0.321 0.634 <0.00 <0.00
HPHPA 118.015 138.085 <0.00 <0.00
2Hydroxhippuric
Acid
1.545 1.799 <0.00
<0.00
HVA and VMA 2.039 0.828 0.002
<0.00
4-Cresol 25.533 28.42 <0.00 <0.00
5-
Hydrocyindoleacti
c Acid
2.197 3.337
<0.00 <0.00
Quinolinic Acid 4.547 2.265 0.001 0.001
KYNA 2.061 1.142 0.002
<0.00
Citramalic 1.963 1.286 <0.00 <0.00
Quinolinic Acid/ 5-
HIAA Ratio
5.129 4.571
<0.00 <0.00
WBC 8.228 2.087 0.067 0.053
Platelets 303.39 85.121 0.15 0.169
Eosinophils 0.5513 1.207 <0.00 <0.00
Lymphocytes 4.563 5.404 <0.00 <0.00
ESR 6.348 6.139 <0.00 <0.00
Zinc 167.288 167.296 <0.00 <0.00

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From the above tables we can see that with the exception of Platelets all of the
results for normality are highly significant indicating that normality is violated.
Therefore the 25th
and 75th
percentiles tell us more information than the mean and
standard deviation, which are based upon normal population samples.
Table 5. Descriptive Statistics – Percentiles for AU Males <13
Measurement 5 10 25 50 75 90 95
Adiposity
Index
0.899 1.054 1.356 1.70
3
2.226 3.293 3.762
Hippuric Acid 27.05 61 100.2
5
221 334.25 616.3 1037.6
5
Succinic Acid 2.41 5.27 8.875 20.5 32.25 63.3 68.65
DHPPA 0.435 0.07 0.1 0.19
5
0.3925 0.553 0.637
HPHPA 4.07 12.5 28.75 73.5 153 254.8 347.7
2Hydroxhipp
uric Acid
0.227 0.32 0.54 1.1 1.9 3.1 5.43
HVA and
VMA
0.933
5
1.1 1.5 1.85 2.425 3.16 3.6
4-Cresol 0.208 1.043 3.3 14.5 39.25 70.1 85.63
5-
Hydrocyindol
eactic Acid
1.635 0.366 0.587 1.05 2.025 5.85 11.65
Quinolinic
Acid
0.585 2 2.9 4.15 5.625 8.03 9.42
KYNA 0.572 0.89 1.3 1.9 2.5 3.56 4.455
Citramalic 0.859 0.717 1.1 1.7 2.4 4.1 4.765
Quinolinic
Acid/ 5-HIAA
Ratio
0.581 0.89 2.2 3.9 6.025 11.3 17.65
WBC 5.162 6.082 6.76 7.78 9.4 11.892 13.456
Platelets 157.2 220.4 231 295 372 437 450.6
Eosinophils 0.092 0.1 0.13 0.25 0.41 0.834 4.986
Lymphocytes 1.614 1.766 2.94 3.37 4.65 5.016 24.124
ESR 2 2 2 5 8 12.2 26.6
Zinc 42.05 54.3 90 140.
5
180.75 268.8 350.95
As the data violates the tests for normality we will use non-parametric statistical
tests. We will use parametric statistical tests when the sample size is greater than 30
because the standard error of the mean will be normally distributed and this is the
criteria for parametric testing.

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Page 38
Figure 5. Histogram of Adiposity Index in AU Males<13
The histogram above shows the shape of the data in the Adiposity Index sample.
This shows that the population is highly positively skewed to the left. Visually we can
see the appearance of outliers with an Adiposity Index of 7. However we can see
that whilst the data is not normally distributed, it is not multi modal. Therefore we
have decided to chunk the data using only the scores between the 25th
and 75th
percentile when analysing results. For histograms of all the data sets see appendix 3
Adiposity Index of Autistic Children Compared with Population Mean
A one-sample t test was used to compare the AI of males<13 with a population
mean. The result was highly significant (p<0.000). This allows us to reject our null
hypothesis and retain the experimental hypothesis that the AI of autistic children will
be significantly elevated when compared with population mean. The sample size
exceeded 30 (n=91) and hence parametric tests were used.
Adiposity Index and Organic Acids Bacterial Markers
Both a Pearson’s and Spearman’s Rho Correlation was run on AI and the bacterial
markers from the OAT. Neither correlation was significant. A MANOVA was then run
on the data to look for any multivariate correlations. It would be suspected that all the
bacterial markers whilst separate measures would inherently be correlated. AI was

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Page 39
chunked on a 3 point ranking as explained above. Again the measures were not
significant (p=. 2). The average effect size is very small (r = 0.008) and even if the
effect size was real it would be clinically meaningless. From this we can reject the
experimental hypothesis and accept the null hypothesis. A MANCOVA was also run.
Whilst participants were already categorised into <13, we wanted to explore whether
there were any further effects of age within this group on the measures, again the
results were insignificant (see appendix 4).
OATS in Autistic Children Compared with the Population Mean
Table 6. OATS in AU Males<13 Compared with the Population Mean –
OATS Marker T df Sig.
(2-
tailed)
Mean
Difference
95%
Confidence
Interval of
the Lower
Difference
95%
Confidence
Interval of
the Higher
Difference
Succinic Acid 5.327 69 0.000 15.535 9.717 21.352
2HydroxyHippuric
Acid
4.561 81 0.000 1.204 0.679 1.729
HVA and VMA 5.771 74 0.000 0.639 0.418 0.859
4-Cresol -3.541 77 0.001 -12.840 -20.060 -5.620
5-HIAA -9.664 76 0.000 -3.281 -3.957 -2.605
Quinolinic/5-HIAA
Ratio
7.163 74 0.000 3.137 2.265 4.010
Citramalic -5.203 81 0.000 -.613 -.848 -.379
From the above table we can see that Succinic acid, 2HydroxyHippuric Acid, HVA &
VMA and the Quinolinic Acid/ 5-HIAA ratio are all significantly elevated in autistic
children when compared with the population means. 4-Cresol, 5-HIAA and Citramalic
are significantly lower when compared to the population mean. Hippuric Acid,
DHPPA, HPHPA, Quinolinic Acid and KYNA were not significantly different from the
population means. We can partly retain the experimental as some not all OATs
markers were elevated in autistic children. For p values of all OATs markers
analysed see appendix 4.
Bacterial Markers and Immune Markers
A one-sample t test was run between each immune marker and the population
mean. Both platelets (t = 3.718, df = 67, p<0.000) and WBC (t=5.617, df=67,
p<0.000) were significantly elevated compared with the population mean.
Eosinophils, ESR, and Lymphocytes were not significantly elevated when compared
with the population (see appendix 4). Parametric testing was used as the sample
size was >30.
Pearson’s correlation found a significant positive correlation between WBC and
HPHPA (p=0.011). However the tests of normality were significant and the sample
size is < 30 so the data is not sufficient to use parametric testing. Therefore this

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correlation coefficient cannot be accepted and Spearman’s Rho correlation must be
used. The correlation was not significant ( see appendix 4).
Significant Spearman’s Rho correlations can be found in the below table
Table 7. Bacterial and Immune Markers in AU Males< 13 – Significant Findings
Hippuric
Acid
Succinic
Acid
HPHPA Citramalic
WBC
Pearson’s Correlation
Coefficient
Sig (2 tailed)
N
n/a .496
.019
22
.508
.007
27
.383
.048
27
Eosinophils
Coefficient
Sig (2 tailed)
N
.421
.026
28
n/a n/a n/a
Lymphocytes
Coefficient
Sig (2 tailed)
N
n/a n/a .408
.031
28
n/a
From the above table we can see the OATS markers correlate positively with the
immune markers. Hippuric Acid, HPHPA and Citramalic are all bacterial markers.
Succinic Acid is a Krebs cycle metabolite. This indicates that as inflammation
increases (as represented by immune markers) so does the amount of pathogenic
bacterial markers. Succinic Acid is the relative riboflavin and/ or CoQ10 deficiency,
suggesting as this deficiency increases so does inflammation. The population
sample for this test is notably smaller than the others. As the study was retrospective
the amount of patients who had undertaken the majority of tests was few. For p
values of all haematology and OAT markers please see appendix 4.
Bacterial Markers, Adiposity Index and Zinc
A one-sample t-test indicated that hair zinc was significantly lower than the
population mean (p=0.001). Again as the population was large (N=112) parametric
testing was used. A Pearson’s correlation found a significant positive correlation
between hair zinc and Succinic Acid (p=0.030). As the sample size was 49, we can
assume Pearson’s Correlation. A Spearman’s Rho correlation did not find any
significant positive correlations. Hair zinc was also positively correlated with
Eosinophils at p<0.000. (For all test results see appendix 4).
Post Hoc Analysis
Exploratory tests were then run to explore correlations and relationships that were
not in line with the central hypothesis. As with the work of de Theije et al. (2014) we
also found 5-HIAA to be significantly decreased in AU. As mentioned in the literature
review gut dysbiosis can result in elevated ileal serotonin but decreased overall

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levels. Parametric Post hoc analysis to see whether it was the gut dysbiosis
potentially affecting the serotonin revealed the following. Again parametric tests
were used as the sample size was >30.
Table 8. Post Hoc Analysis: Bacterial OAT Markers and 5-HIAA – Significant
Findings
Bacterial OAT Marker 5-HIAA
HPHPA
Pearson’s Correlation Coefficient
Sig (2 tailed)
N
.271
0.017
77
2HydroxyHippuric Acid
Sig (2 tailed)
N
.306
0.007
77
Citramalic
Sig (2 tailed)
N
.225
.049
77
As we can see from the table 8, 5-HIAA is significantly positively correlated with
three bacterial OATs markers: HPHPA, 2HydroxyHippuric Acid and Citramalic.
Parametric post hoc analysis was also run on the links between Quinolinic acid and
the bacterial markers.
Table 9. Post Hoc Analysis: Bacterial OAT Markers and Quinolinic Acid –
Bacterial OAT Marker Quinolinic Acid
2HydroxyHippuric Acid
Sig (2 tailed)
N
.274
.017
75
Citramalic
Sig (2 tailed)
N
.397
.000
75
From the above we can see that Quinolinic acid is significantly positively correlated
with specific bacterial OAT markers.

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Page 42
Table 10. Table of Hypotheses
Null Hypothesis Retain or Reject the Null
Hypothesis
1 There will be no difference between the AI
of AU children when compared with the
general population.
REJECT
2 There will be no correlation with bacterial
OATS markers and adiposity index in
autistic children. Age will not have an
effect.
RETAIN
3 There will be no difference between OATS
markers in autistic children when compared
with the population mean.
REJECT
4 There will no relationship between bacterial
markers in the OATS and raised
inflammatory markers.
REJECT
5 There will be no relationship between zinc,
adiposity index and OATS markers.
REJECT
2.3 Discussion of Study Findings
Adiposity Index of Autistic Children Compared with Population Mean
The adiposity of AU males was significantly different from the population mean, in
that the levels of Firmicutes were raised significantly compared with Bacteroides. It
may therefore be that pathogenic bacteria are a necessary component of ASD. The
literature review in chapter 1 found a mixed result of elevation in AI, as mentioned
previously this is likely due to age. Therefore our findings may only be relevant to
males<13.
Adiposity Index and Organic Acids Bacterial Markers
There was no significant correlation with AI and the bacterial OAT markers. It would
have been expected that children with a pathogenic gut flora as measured by AI
would also result in elevated markers of gut dysbiosis on the OAT. However the AI
looks at dysbiosis at the end of the of the large intestine, whereas the OAT markers
are looking at the end metabolite of mainly of small intestine gut bacteria activity. It
may be that there is no OAT marker that correlates with large intestine dysbiosis as
new bacterial markers are found yearly. It is also possible that there may be
competition between the pathogenic gut bacteria. Ultimately as shown by Williams et
al. (2014) it is the metabolic activity of the gut microflora that is important.
Unfortunately the stool test does not provide information on this.

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Page 43
OATS in Autistic Children Compared with the Population Mean
HVA & VMA were significantly elevated in AU males<13. VMA is the urinary
metabolite of the catecholamines whereas HVA is that of dopamine. Elevated levels
of these indicate chronic stress, as there is an increased rate of production and
breakdown of cortisol. Within the context of AI and gut flora, this is fitting with our
hypothesis as stress can have a long term damaging effect on these. A study on 25
patients who had experienced stress in terms of burn, trauma and sepsis were found
to have between log2 and log4 lower amounts of the beneficial bacteria;
Bifidobacterium and Lactobacillus. They also had log2 higher amounts of the
pathogenic strains: Staphylococcus and Pseudomonas (Shimizu et al., 2006).
Shimizu et al. (2006) also found a significant decrease in the OATS markers of
beneficial markers e.g. butyric acid and a significant increase in pH. Stress related
disorders such as depression have been found to have a translocation of bacteria
representative of a leaky gut (Maes et al., 2012). Supporting our mechanism that
neurotransmitters produced as a result of gut bacteria can lead to impaired gut
integrity. Interestingly toxic exposure to aluminum can affect the metabolism of
theses neurotransmitters and an elevated HVA & VMA can also be representative of
this. Literature repeatedly links ASD and toxic measure exposure; a review by Seneff
et al. (2012) using empirical data found that AU children were significantly vulnerable
to toxic metals including aluminum. Current work on hair minerals in AU children by
Sehinson (2015) could support this hypothesis.
5-HIAA was found to be significantly correlated with specific bacterial OATS
markers. Of particular interest was the significant correlation with HPHPA, which
represents an overgrowth of certain Clostridia strains. This supports the notion that
TeNT can reduce serotonin uptake as put forward by Humeau et al., (2000). In
addition to this HPHPA also inhibits the dopamine-beta-hydroxylase enzymes helps
explain the elevated HVA and VMA recorded.
Specifically it was the 5-HIAA/Quinolinic ratio that was found to be significantly
elevated in autistic males<13. Quinolinic Acid has been expressed as the “critical link
between the immune system and the brain” (Lord & Bralley, 2012). Viral stimulation
of the immune system results in the release of IFNγ, quinolinic acid then interacts
with the NDMA receptors of the glutamatergic neurons that response to pain. An
inflamed GI tract is a source of IFNγ suggesting that this elevated response may
represent dysbiosis as well as neuronal deterioration. Post hoc analysis supported
this notion.
In addition to elevated NT and bacterial OAT marker, Succinic acid was found to be
significantly elevated when compared with the population mean. Succinic acid is a
Krebs cycle metabolite and the elevation is suggestive of a riboflavin and/or CoQ10
deficiency. Succinic Acid is an important cofactor in the citric acid cycle, as it is
needed to sustain levels of FAD. These deficiencies lead to ineffective energy
production and highlight abnormal mitochondrial fatty acid processing. Symptoms of
deficiency include neurological deterioration as well as fatigue and clinically riboflavin
administration has been shown to regress neurological impairment in boys under 5
with Leigh syndrome (Pinard et al., 1999).

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Page 44
2HydroxyHippuric Acid was significantly elevated suggesting an overall intestinal
dysbiosis in AU males<13 particularly in salicylate producing GI bacteria. 4-Cresol
was significantly lower when compared with the general population. This was not
expected as this metabolite is commonly elevated in AU children. This metabolite
also inhibits dopamine-beta-hydroxylase, which leads to an imbalance of NTs, which
was supported by the finding of elevated HVA & VMA. It may be that competition
between pathogenic bacteria lead to a reduced findings. Particularly as this is
commonly seen in AU children, it may be that these children had already been
treated for this, as children attending SH tend to have had multiple interventions. In
addition we also saw decreased Citramalic. Citramalic is representative of
saccharomyces or propionibacteria. Decreased levels could be for the same reasons
as given for 4-Cresol.
Bacterial Markers and Immune Markers
Both WBC and platelets were significantly increased in AU Males<13. WBC are
usually increased in the initial stages of disease or in acute bacterial/viral infections.
In terms of viral infection we would expect increased lymphocytes also which was
not found. In acute bacterial infection we would expect to see normal lymphocytes
(which was found) supporting the bacterial dysbiosis found. Any time epinephrine is
elevated in the body WBC also tends to increase. The fact that the AU males may be
highly stressed is also supported by the increased urinary output of HVA & VMA.
Increased WBC also accompanies intestinal parasites and we found elevated AI in
these children as well as elevated bacterial OAT markers.
Increased platelet production is usually indicative of atherosclerosis. However it is
also associated with inflammation and excessive antioxidant stress (Weatherby and
Ferguson, 2002). Inflammation in the gut as well as increased gut permeability can
lead to increased Nitric Oxide production which would put the antioxidants in the
body under stress (Maes., 2008).
We found that Eosinophils were significantly positively correlated with Hippuric acid.
Eosinophils are part of the immune system that deals with allergies and/or parasite
infection. Hippuric Acid is indicative of GI dysbiosis, as it is a bacterial product of
phenylalanine metabolism. Therefore the rise in Eosinophils may be as a result of
this infection.
WBC count is positively correlated with Succinic Acid, HPHPA & Citramalic. Both
HPHPA and Citramalic are markers of GI dysbiosis. This suggests that as GI
dysbiosis increases, as does tissue inflammation. It also suggests that as nutrient
deficiencies and impaired mitochondrial function increase do does tissue
inflammation. However the direction of causality for either cannot be determined.
Lymphocytes were also positively associated with HPHPA. HPHPA is a marker for
clostridia and lymphocytes are suggestive of viral infection. It may be that GI
infection and/or dysbiosis weakens the immune system leaving the individual
vulnerable to viral infection. However causation cannot be implied at this point. It has
also been shown that infections whether viral, bacterial or from yeast can effect GI
integrity and cause intestinal permeability. An In vitro study by Grisham et al. (1990)

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found that neutrophil derived oxidants negatively effected epithelial cell integrity and
resulted in diarrhoea in IBD.
Bacterial Markers, Immune Markers and Zinc
The results show that as hair zinc levels increase so does eosinophil count. A high
eosinophil count is suggestive of parasite infection and/or an allergic reaction. A high
hair zinc result is not always indicative of adequate or high zinc levels. It is often due
to a high presence of toxic metals in the body. Copper displaces zinc and it is likely
this displacement may result in high levels in the hair. Therefore the increase in hair
zinc in this subset could be due to the presence of toxic metals and hair zinc may be
indicative of low serum zinc. Further research would be needed to confirm this
hypothesis. Succinic Acid is a Krebs cycle metabolite that shows a B2 and/or CoQ10
deficiency. It is also positively associated with zinc. The larger the elevation in
succinic acid the greater the deficiency, again supporting that AU children are
vitamin and mineral deficient and high hair zinc may be representative of low serum
zinc.
2.4 Implications
Table 11. Nutritional Interventions based on OAT Markers
Organic Acid Elevated/ Decreased Intervention
Succinic Acid Elevated Supplement with CoQ10
and Magnesium
(500mg)
HVA and VMA Elevated Supplement with
1000mg tyrosine
between meals and
phenylalanine
hydroxylase cofactors
as needed
Quinolinic Acid Elevated Supplement with 100mg
B6 and 300mg
Magnesium.
Bacterial (Gut
Dysbiosis)
Elevated 10-20mg Glutamine
daily. Include digestive
enzymes and remove
potential irritants from
diet.
5-HIAA Decreased & Elevated 5-HTP,300mg
Magnesium, 100mg B6.
Adapted from Lord & Bralley, 2012.
Table 8 is extracted from the Laboratory Evaluations textbook for Integrative and
Functional Medicine (Lord & Bralley, 2012). These nutritional supplements
recommended are based upon the metabolic pathway that is impaired. Based upon
the elevated markers they postulate impaired metabolisms regarding: renal ammonia
clearance, Epinephrine, norepinephrine and DOPA catabolism, inflammation –

________________________________________________________________
Page 46
stimulated macrophage pathway, intestinal bacterial overgrowth and serotonin
catabolism respectively. As the markers are all functional markers for the metabolic
effects of nutrient inadequacies as well as toxic exposure, the aim is to make
corrections at the metabolic level. Supplementing males<13 with AU with these
supplements upon diagnosis may lead to a reduction in symptoms by supporting the
impaired pathways. As our study is correlational there is not sufficient evidence for
the use of these preventatively.
2.5 Strengths and Limitations of Methodology
By using pre existing medical records the sample sizes of the populations were
highly restricted as only those who had sufficient data could be included. This meant
that some tests had noticeably small sample sizes and we were only able to look at
relationships in males<13. Further age grouping within this sample may be needed.
Mariat et al., (2009) noted age related changes in the Firmicutes/Bacteroides ratio.
However the benefit of this method of testing was that there was no cost implications
involved nor was there any inconvenience to patients. Patients who had requested
on their medical questionnaires for their results to be kept confidential and restricted
from scientific research were excluded from the study. By using these records the
study is easily replicable and investigations looking at a variety of relationships is a
possibility.
By taking the first test drawn we have the opportunity to conduct longitudinal studies
looking at trends and the impact of nutritional interventions on blood chemistry and
symptomology. However whilst we took the first test from each sample, there is no
guaranteeing that that the first of each test (e.g. blood and OAT) were taken at the
same time. Therefore we may have been looking for correlations between tests that
are representative of different time periods and therefore different symptomatology.
This may be one reason as to why a lack of significance was experienced. However
as testing is recommend by the health practitioner it is likely that tests would have
been conducted within an appropriate timeframe of each other. Time and resource
permitting, replication of this study allowing for this would be insightful.
By using retrospective medical data we were able to choose which tests to include
and could ensure that well-established laboratories undertook all testing. However
the reference ranges given by private laboratories are often more sensitive than
those given by the NHS. As we only compared OATS markers with reference rage
this should not cause any issue. However if generalisations were made based upon
these results to the ASD populations for any interventions, these may need to be re-
evaluated. In addition to this a retrospective design is more prone to bias than a
prospective study and are only appropriate if a prospective study is not feasible
(Hess, 2004).
The majority of the tests used to explore the data were correlational. Correlations
provide information into the relationships within the data and help us to explore
biochemical mechanisms, and overall help to support Nutritional Therapy as a field.
Unfortunately they are limited to the direction of the relationship and we are not able
to infer causality.

131049 MSc PNRD

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