This is not the most recent version of the article. View current version (11 MAY 2015)

Intervention Protocol

You have free access to this content

Chelation for autism spectrum disorder (ASD)

  1. Stephen James1,*,
  2. Katrina Williams2,3,4,
  3. Natalie Silove5,
  4. Shawn W Stevenson6

Editorial Group: Cochrane Developmental, Psychosocial and Learning Problems Group

Published Online: 11 OCT 2013

DOI: 10.1002/14651858.CD010766


How to Cite

James S, Williams K, Silove N, Stevenson SW. Chelation for autism spectrum disorder (ASD) (Protocol). Cochrane Database of Systematic Reviews 2013, Issue 10. Art. No.: CD010766. DOI: 10.1002/14651858.CD010766.

Author Information

  1. 1

    Arizona State University, School of Social Work, Phoenix, Arizona, USA

  2. 2

    University of Melbourne, Department of Paediatrics, Parkville, Victoria, Australia

  3. 3

    Royal Children's Hospital, Department of Developmental Medicine, Parkville, Victoria, Australia

  4. 4

    Murdoch Childrens Research Institute, Parkville, Victoria, Australia

  5. 5

    The Children's Hospital at Westmead, Child Development Unit, Westmead, New South Wales, Australia

  6. 6

    University of Melbourne, The Royal Children's Hospital, Department of Paediatrics, Parkville, Victoria, Australia

*Stephen James, School of Social Work, Arizona State University, 411 N. Central Avenue, Suite 800, Phoenix, Arizona, 85004-0689, USA. stephen.james@green.oxon.org. stephen.nicholas.james@gmail.com.

Publication History

  1. Publication Status: Edited (no change to conclusions)
  2. Published Online: 11 OCT 2013

SEARCH

This is not the most recent version of the article. View current version (11 MAY 2015)

 

Background

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support
 

Description of the condition

According to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), autism spectrum disorders (ASD) are a set of pervasive neurodevelopmental conditions that are characterised by difficulties in social interaction and communication, and the presence of restricted, repetitive behaviours (i.e. stereotypies) (APA 2012). Historically, Asperger’s disorder, autistic disorder, atypical autism, and pervasive developmental disorder not otherwise specified (PDD NOS) were considered examples of conditions falling within the autism spectrum. However, the latest edition of the DSM (DSM-5) now uses the term ASD for diagnostic classification (APA 2012).

The severity of ASD varies considerably, and there is great variability in symptoms and manifestations. Individuals with ASD have difficulty in social-emotional reciprocity (e.g. such as participating in reciprocal conversations or maintaining eye gaze), communicating verbally and non-verbally, forming and maintaining relationships, and understanding the social behaviour of others (Shattuck 2007; APA 2012). Individuals with ASD also exhibit preoccupations and restricted, repetitive patterns of interest and behaviours, which may include strong adherence to routines and stereotyped speech and motor movements (Lecavalier 2006; APA 2012). Some may present with behavioural symptoms (e.g. such as irritability, aggression, anxiety, self-injury, and hyperactivity), but these features are not part of diagnosis, rather they are co-occurring symptoms. The specific causes of ASD are currently unknown. However, genetic factors and prenatal and perhaps postnatal environmental factors are believed to contribute to the onset of ASD, although the role of environmental triggers remains uncertain (Hallmayer 2011). 

There are many interventions for ASD, and while some such as early intensive intervention are effective in improving communication, social interaction, and behaviours, none are capable of producing complete remission of all symptoms. Pharmacologic interventions are often prescribed to individuals with ASD, primarily to target specific associated symptoms or co-occurring features. Currently, however, no pharmacologic interventions target core symptoms of ASD. Individuals with ASD also frequently use complementary and alternative medicine (CAMs); approximately 75% of children with ASD use CAMs (Hanson 2007). Examples of CAMs that are used for ASD include exclusion diets, essential fatty acids, multi-vitamins, acupuncture, auditory integration training, and chelation therapy. To date, there is no consistent evidence that CAMs are effective interventions for core features and associated behaviours of ASD (Nye 2005; Gold 2006; Millward 2008; Cheuk 2011; Sinha 2011; James 2011).

 

Description of the intervention

Chelation therapy involves the administration of a chelating substance that binds to heavy metals, such as lead and mercury, and is then excreted in urine. Chelating substances are approved for treating cases of heavy metal poisoning. However, they have also been used for unapproved reasons and conditions such as Alzheimer’s disease, coronary heart disease, and ASD (Ernst 2000; Dans 2002; Sinha 2006; Hedge 2009). Types of chelating substances used to reduce heavy metal poisoning are outlined in  Table 1 . The administration of chelation therapy for approved uses occurs in a very controlled environment, which is different to the process followed by practitioners administering chelation for unapproved uses, such as for ASD.

Cases of heavy metal poisoning, such as acute lead poisoning, require urgent hospitalisation and administration of chelating substances either intravenously or by deep intramuscular injection for four hours. During the treatment of acute lead poisoning, blood and urine are monitored constantly, as significant shifts of the heavy metal can occur between the blood and central nervous system with dire consequences. Moreover, because minerals and metal ions are essential elements that serve important functions in multiple biological processes, excessive removal can result in deleterious consequences; for example, recently a child with ASD experienced fatal myocardial necrosis resulting from hypocalcaemia after receiving chelation therapy (Brown 2006). Additionally, the local health department is notified, as the source of the lead needs to be identified and dealt with appropriately.

The unapproved use of chelation therapy, for example when used as an intervention for ASD, may involve practitioners using various chelating substances and unlicensed routes of administration (e.g. such as through the rectum or skin) to remove reported excess levels of mercury or other heavy metals or both (Semple 2011). Prior to treatment, individuals with ASD may undergo preliminary tests with the chelating substance to evoke a response, followed by timed urine collection to determine the levels of heavy metals in the body (Bradstreet 2003; Adams 2009). One of the more commonly used chelating substances, oral dimercaptosuccinic acid (DMSA; also called succimer), is being given on a cyclical basis at doses of 10 mg/kg/day every eight hours for three days, followed by 11 days with no DMSA (Bradstreet 2003; Adams 2009). These two-week cycles are then repeated up to six times, totaling approximately three months of treatment (Adams 2009).

Between 6% and 11% of families of children with ASD in various English speaking countries, including the United States, Canada, and Australia, have sought out and tried chelation therapy, and most of these families perceived chelation therapy to result in improvement of symptoms (Green 2006; Goin-Kochel 2009; Christon 2010).

 

How the intervention might work

The theoretical basis for mercury or other heavy metals causing ASD draws on a wide variety of hypotheses, none yet confirmed. One hypothesis is that mercury or other heavy metals are present in greater levels in children with ASD, compared to their peers, as a result of intrauterine exposure to maternal stores or intake, increased intake from immunisations (thimerosal), oral ingestion (fish or medication), inhalation (airborne pollution), increased absorption, altered metabolism, or decreased excretion (Bernard 2001; Goldman 2001; Holmes 2003; Levy 2003; Counter 2004; Kern 2007).

The excess of stored or circulating total body mercury or other heavy metals is thought to interfere with developmental processes implicated in ASD, and it has been suggested that symptoms of mercury poisoning and ASD share some characteristics (Bernard 2001).  Mercury, in being able to cross the blood-brain barrier and the placental barrier, can affect the nervous system and disrupt normal development of the foetus (Aschner 1990; Liu 2008). Prenatal mercury poisoning may result in neurological impairments and global developmental delay and intellectual disability, while postnatal exposure can result in memory loss, irritability, fatigue, intention tremor, skin discolouration, and other organ involvement, including kidney dysfunction (e.g. nephrotic syndrome or tubular dysfunction or both) (Bakir 1973; Amin-Zaki 1974; Grandjean 1997; Goldman 2001; Counter 2004).

Research has produced contradictory findings with regard to levels of heavy metals in individuals with ASD. Although some studies found higher levels of heavy metals in individuals with ASD compared to individuals without ASD (Cohen 1976; Cohen 1982; Adams 2013), more emerging research found no association between ASD and higher levels of heavy metals (Hertz-Picciotto 2010; Albizzati 2012; Rahbar 2013).

An alternative hypothesis is that mercury or other heavy metals could cause ASD through altered cellular functioning that does not require increased body stores or circulating mercury or other heavy metals. In this context, it is thought that individuals with ASD have an impaired capacity to excrete heavy metals and that the severity of autism symptomatology is inversely correlated with excretion ability (Holmes 2003; Kern 2007). This hypothesis is currently being explored (Deth 2008; Zecavati 2009; Garrecht 2011), but it is unlikely that chelation therapy would be beneficial in this context.

 

Why it is important to do this review

Novel therapies are used frequently by individuals with ASD (Hanson 2007). Despite their increasing use, most CAMs for ASD lack a robust evidence base (Nye 2005; Gold 2006; Millward 2008; Cheuk 2011; Sinha 2011; James 2011). Chelation therapy is one CAM that continues to be used and promoted as efficacious, despite being discouraged by physicians (Golnik 2009) and reports of harm, including death (Brown 2006). A systematic review that examines potential beneficial and harmful effects of chelation for symptoms of ASD is urgently needed to provide best evidence to inform future decision-making about the use of this intervention. Results from this systematic review will help families with ASD make well-informed decisions about the use of chelation therapy and will assist relevant services and other organisations make decisions about best practice.

 

Objectives

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

To assess the potential benefits and adverse effects of chelation for ASD symptomatology.

 

Methods

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support
 

Criteria for considering studies for this review

 

Types of studies

Randomised controlled trials.

 

Types of participants

Individuals of any age diagnosed with ASD using established diagnostic criteria (e.g. DSM-IV) or standardised diagnostic instruments (e.g. the Childhood Autism Rating Scale, Autism Diagnostic Observation Scale, or Autism Diagnostic Interview - Revised) will be eligible for inclusion.

 

Types of interventions

Interventions will include chelating substances of any type and dose, regardless of administration frequency or method, compared to placebo. We will include trials if the chelating substances are provided alone or as an adjunctive treatment compared to placebo (e.g. chelation in combination with a behavioural intervention versus placebo in combination with a behavioural intervention).

 

Types of outcome measures

 

Primary outcomes

  1. Core symptoms of ASD, using any measure
    1. Social interaction*
    2. Communication*
    3. Stereotypy*
  2. Adverse events*

 

Secondary outcomes

  1. Non-core behaviours, using any measure
    1. Irritability*
    2. Aggression
    3. Hyperactivity*
    4. Insomnia
    5. Self-injury
  2. Quality of life for individual or family*
  3. Heavy metal levels

Asterisked (*) outcomes will be included in a 'Summary of findings' table.

For our primary analysis, we will assess outcomes at immediate post-intervention (i.e. completion of treatment).

 

Search methods for identification of studies

 

Electronic searches

We will search the following databases without imposing language or date restrictions:

  1. The Cochrane Central Register of Controlled Trials (CENTRAL), part of the Cochrane Library;
  2. MEDLINE;
  3. EMBASE;
  4. CINAHL;
  5. PsycINFO;
  6. Science Citation Index;
  7. Social Science Citation Index;
  8. Conference Proceedings Citation Index – Science;
  9. Conference Proceedings Citation Index – Social Science and Humanities;
  10. Autism Data (www.autism.org.uk/autismdata);
  11. WorldCat (limited to theses and dissertations) www.worldcat.org/;
  12. metaRegister of Controlled Trials metaRegister of Controlled Trials;
  13. ClinicalTrials.gov (clinicaltrials.gov/);
  14. ICTRP (apps.who.int/trialsearch/);
  15. TOXNET (toxnet.nlm.nih.gov/);
  16. LILACS;
  17. Scieclo; and
  18. Google Scholar.

We will search Ovid MEDLINE using the following search strategy, which we will adapt as necessary for other databases:

1 exp child development disorders, pervasive/

2 Developmental Disabilities/

3 pervasive development$ disorder$.tw.

4 (pervasive adj3 child$).tw.

5 (PDD or PDDs or PDD-NOS or ASD or ASDs).tw.

6 autis$.tw.

7 asperger$.tw.

8 kanner$.tw.

9 childhood schizophrenia.tw.

10 Rett$.tw.

11 or/1-10

12 Chelation Therapy/

13 Chelating Agents/

14 2,2'-dipyridyl/ or caseins/ or chitosan/ or citric acid/ or cuprizone/ or dimercaprol/ or dithizone/ or ditiocarb/ or edetic acid/ or egtazic acid/ or fura-2/ or humic substances/ or nitrilotriacetic acid/ or penicillamine/ or pentetic acid/ or phytochelatins/ or razoxane/ or succimer/ or technetium tc 99m pentetate/ or thenoyltrifluoroacetone/ or trientine/ or unithiol/

15 Iron Chelating Agents/

16 siderophores/

17 ferrozine/ or pentetic acid/ or deferoxamine/ or enterobactin/

18 Fursultiamin/ or (Fursultiamin$ or TTFD or thiamine tetrahydrofurfuryl disulfide).tw.

19 complexon$.tw.

20 (chelation or chelating or chelator$).tw.

21 (metal adj3 antagonist$).tw.

22 ("2,2'-Dipyridyl" or alcaligin or antipyrylazo or arsenazo or bixalomer or cabiotraxetan or calcobutrol or caldiamide or calixarene or calteridol or caloxetic acid or "carboxymethyl beta cyclodextrincaseins" or catenane or chitson or choline tetrathiomolybdate or citric acid or clathrin$ or (crown adj (compound or ether)) or cyclodextrin or cyclophane or cuprizone).mp.

23 (dimercaprol or dimercaptosuccinic acid or DMSA or deferasirox or deferiprone or deferitrin or deferoxamine or deferriferrithiocin or diglycine or dimercaprol or dimethyldithiocarbamate or dithizone or ditiocarb).mp.

24 (edetate or Edetic Acid or Egtazic Acid or Fura-2 or Humic Substances or Nitrilotriacetic Acid or Penicillamine or Pentetic Acid or phytic acid or Phytochelatins or Razoxane or salicylhydroxamic acid or sugammadex or Succimer or "Technetium Tc 99m Pentetate " or Thenoyltrifluoroacetone or Trientine or triethylenetetramine$ or tropantiol or Unithiol or versetamide).mp.

25 (siderophores or ferrozine or pentetic acid or deferoxamine or enterobactin).mp.

26 or/12-25

27 11 and 26

28 exp animals/ not humans.sh.

29 27 not 28

 

Searching other resources

We will search reference lists from the retrieved articles for studies not already identified, and we will contact known experts in the field to enquire about other sources of information. We will also search relevant websites including Autism Speaks (http://www.autismspeaks.org/), Research Autism (http://researchautism.net/) and the United States Department of Health and Human Services (http://www.hhs.gov/).

 

Data collection and analysis

 

Selection of studies

Two authors (SJ and SS) will independently screen the titles and abstracts of the citations identified from the search. SJ and SS will then obtain the full text of studies that meet, or seem likely to meet, the inclusion criteria. The other members of the review team (NS and KW) will act as arbiters in the event of dispute.

 

Data extraction and management

Two authors (SJ and SS) will independently extract data from the included studies using a data extraction form designed and piloted for this review. The following information will be extracted:

  1. Study methods and setting;
  2. Participant details;
  3. Intervention details; and
  4. Outcomes.

We will resolve disagreements through consultation with the other authors (NS and KW).

SJ will enter data into Review Manager 5.2 (Review Manager 2012), which will be checked by SS.

 

Assessment of risk of bias in included studies

Two authors (SJ and SS) will independently assess the risk of bias in the included studies using the tool described in the Cochrane Handbook of Systematic Reviews of Interventions (Higgins 2011, 8.5). For each study we will judge the risk of bias to be either low, high, or unclear risk for each of the following domains.

  1. Sequence generation
    • low risk: if a random component was used in the sequence generation process, such as coin-tossing, computer-generated random numbers, or a table of random numbers.
    • high risk: if a non-random component was used in the sequence generation process.
    • unclear: the sequence generation process was not described.
  2. Allocation concealment
    • low risk: if participants and trial investigators had no foreknowledge (i.e. prior to eligibility decisions being made and informed consent being obtained) of intervention assignment through the use of, for example, central allocation or sequentially numbered envelopes that were opaque and sealed.
    • high risk: if participants and trial investigators had foreknowledge of intervention assignment.
    • unclear: the method of allocation concealment was not described.
  3. Blinding of participants and personnel
    • low risk: if there was no blinding or incomplete blinding but review authors judge the outcome is unlikely to have been influenced by lack of blinding, or if blinding of study participants and personnel was ensured and it is unlikely that blinding could have been broken.
    • high risk: if there was no blinding or incomplete blinding and the outcome was likely influenced by a lack of blinding, or if blinding of study participants and personnel was attempted but it is likely that blinding could have been broken and the outcome influenced by lack of blinding.
    • unclear: if lack of information prohibits judgement of either low or high risk of bias, or if the study did not address this outcome.
  4. Blinding of outcome assessment
    • low risk: if there was no blinding of outcome assessment but review authors judge the outcome measurement is unlikely to have been influenced by lack of blinding, or if blinding of outcome assessment was ensured and it is unlikely that blinding could have been broken.
    • high risk: if there was no blinding of outcome assessment and the outcome measurement was likely influenced by a lack of blinding, or if there was blinding of outcome assessment but it is likely that blinding could have been broken and the outcome measurement influenced by lack of blinding.
    • unclear: if lack of information prohibits judgement of either low or high risk of bias, or if the study did not address this outcome.
  5. Incomplete outcome data
    • low risk: if no missing data were reported or if appropriate methods were used to impute missing data, or if the reason for missing data is unlikely to be related to the true outcome.
    • high risk: if missing data were reported and no appropriate methods were used to impute missing data, or if the reason for missing data is likely to be related to the true outcome.
    • unclear: if lack of information prohibits judgement of either low or high risk of bias, or if the study did not address this outcome.
  6. Selective reporting
    • low risk: if a study has a protocol and all pre-specified outcomes were reported in the pre-specified manner, or if a study has no protocol but all expected outcomes have been reported.
    • high risk: if a study has a protocol and one or more pre-specified outcomes were either not reported or were reported in a manner that was not pre-specified, or if a study has no protocol and all expected outcomes have not been reported.
    • unclear: if lack of information prohibits judgement of either low or high risk of bias.
  7. Other bias
    • low risk: if other sources of bias (e.g. contamination or recruitment bias) do not appear to exist.
    • high risk: if other sources of bias exist.
    • unclear: if lack of information permits judgement of whether other sources of bias exist.

We will resolve disagreements by reaching consensus with the help of the other review authors (NS and KW).

 

Measures of treatment effect

 

Dichotomous data

We will analyse dichotomous outcomes by calculating the risk ratio (RR) and corresponding 95% confidence interval (CI).

 

Continuous data

For continuous outcomes we will calculate mean differences (MD) and corresponding 95% CI if studies use the same rating scales. As recommended by Higgins 2011, we will focus on final values unless change scores are used in some of the studies. We will combine in the same meta-analysis studies that report final values with studies that report only change scores, provided that the studies use the same rating scale (Higgins 2011). A potential problem of including change scores is that the standard deviation of changes may not be reported in the original study (Higgins 2011). We will contact trial authors and attempt to estimate the standard deviation of changes if it is not reported. We will calculate the standardised mean difference (SMD) with 95% confidence intervals if studies use different scales to measure the same outcomes.

 

Multiple outcomes

If studies provide multiple, interchangeable measures of the same construct at the same point of time, we will calculate the average SMD across the outcomes and the average estimated variances (Higgins 2011).

 

Unit of analysis issues

We will incorporate cross-over trials into meta-analysis using results from paired analyses (Elbourne 2002; Higgins 2011). We do not anticipate finding cluster-randomised trials. We will create a single pair-wise comparison for each identified multi-arm study by combining all relevant experimental groups into a single group, and by combining all relevant control groups into a single group (Higgins 2011).

 

Dealing with missing data

We will attempt to contact trial investigators to request missing data. If missing data are provided by the trialists, we will conduct meta-analysis according to intention-to-treat principles using all data and keeping participants in the treatment group to which they were originally randomised, regardless of the treatment they actually received (Higgins 2011). If missing data are not provided, we will analyse only the available data, and we will not impute missing data given that symptoms of ASD vary greatly. We will document missing data and attrition in the 'Risk of bias' table, and we will discuss how missing data may affect the interpretation of the results.

 

Assessment of heterogeneity

We will assess clinical heterogeneity by comparing the between-trials distribution of participant characteristics (e.g. children versus adults), trial characteristics (e.g. cross-over versus parallel design), and intervention characteristics (e.g. treatment type and dose). We will evaluate statistical heterogeneity using the I2 statistic and the Chi2 test of heterogeneity, with statistical significance set at P < 0.10. We will consider I2 values as follows:

  • 0% to 29% might not be important;
  • 30% to 49% may represent moderate heterogeneity;
  • 50% to 74% may represent substantial heterogeneity;
  • 75% to 100% represents considerable heterogeneity (Higgins 2011).

 

Assessment of reporting biases

If 10 or more studies are found, we will use funnel plots to investigate the relationship between intervention effect and study size. Asymmetry of a funnel plot may indicate, among other things, publication bias or poor methodological quality (Egger 1997). We will explore possible reasons for any asymmetry found.

 

Data synthesis

We will synthesize results in a meta-analysis using a fixed-effect model when studies are similar enough with regard to the intervention, population, and methods to assume that the same treatment effect is estimated.  We will synthesize results in a meta-analysis using a random-effects model when statistical heterogeneity is found or when studies differ enough with regard to the intervention, population, and methods to assume that different yet related treatment effects are estimated, and when it is deemed to be clinically relevant (Higgins 2011).

 

Subgroup analysis and investigation of heterogeneity

We will conduct the following subgroup analyses:

  1. Type of ASD (e.g. autistic disorder versus Asperger's disorder);
  2. Participant age (e.g. adults versus children, preschool versus school-age);
  3. Treatment type (e.g. DMSA versus other agents);
  4. Treatment dosage (e.g. DMSA dose of 10 mg/kg bodyweight administered three times per day versus higher doses); and
  5. Length of follow-up (e.g. ≤ 3 months versus > 3 months).

 

Sensitivity analysis

We will conduct sensitivity analyses to investigate the effect on the overall results of excluding trials where:

  1. Allocation concealment or sequence generation was inadequate (selection bias);
  2. Blinding was not done (performance bias); and
  3. Outcome data were incomplete (attrition bias).

 

Acknowledgements

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

We would like to thank Laura MacDonald and Geraldine Macdonald for their feedback and guidance. We would also like to thank Margaret Anderson for her assistance with the development of our search strategy.

 

Contributions of authors

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

All authors contributed to the authorship of the protocol.

 

Declarations of interest

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

Stephen James - none known.

Katrina Williams - I gave a talk about treatments for autism at a symposium organised by Janssen-Cilag Pty Ltd. Janssen-Cilag had no control over the contents of the talk and the speaker's fee was paid to the University that employs me. I do not have an ongoing relationship with Janssen-Cilag.

Natalie Silove - Children's Hospital Westmead is enrolling up to eight participants in a phase two drug trial in adolescence with fragile x syndrome. It is not related to autism or to chelation. I receive no personal funds at all.

Shawn Stevenson - At the time of this review, I was employed by Autism Victoria in the information and training services team delivering professional development workshops and training to people who work with people affected by an autism spectrum disorder. I was also an unpaid Board Member of Support and Advocacy Autism Individuals and Families.

 

Sources of support

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support
 

Internal sources

  • University of Melbourne, Australia.
    Shawn Stevenson received a salary through support of the Williams Collie Trust, University of Melbourne, for autism research during the time of this protocol. Katrina Williams received a salary through support of the APEX Australia Chair of Developmental Medicine, University of Melbourne. The University provided IT, office and library facilities during the time of this protocol.
  • NSW Health Service, Australia.
    Natalie Silove's salary is paid for by NSW Health Service - The Sydney Children's Hospital Network

 

External sources

  • No sources of support supplied

References

Additional references

  1. Top of page
  2. Abstract
  3. Background
  4. Objectives
  5. Methods
  6. Acknowledgements
  7. Contributions of authors
  8. Declarations of interest
  9. Sources of support
  10. Additional references
Adams 2009
  • Adams JB, Baral M, Geis E, Mitchell J, Ingram J, Hensley A, et al. Safety and efficacy of oral DMSA therapy for children with autism spectrum disorders: Part A -- medical results. BMC Clinical Pharmacology 2009;9:16.
Adams 2013
  • Adams JB, Audhya T, McDonough-Means S, Rubin RA, Quig D, Geis E, et al. Toxicological status of children with autism vs. neurotypical children and the association with autism severity. Biological Trace Element Research 2013;151(2):171-80.
Albizzati 2012
  • Albizzati A, More L, Di Candia D, Saccani M, Lenti C. Normal concentrations of heavy metals in autistic spectrum disorders. Minerva Pediatrica 2012;64(1):27-31.
Amin-Zaki 1974
APA 2012
  • American Psychiatric Association. DSM-5 Development. www.dsm5.org (accessed 10 January 2013).
Aschner 1990
Bakir 1973
Bernard 2001
Bradstreet 2003
  • Bradstreet J, Geier DA, Kartzinel JJ, Adams JB, Geier MR. A case-control study of mercury burden in children with autistic spectrum disorders. Journal of American Physicians and Surgeons 2003;8(3):76-9.
Brown 2006
Cheuk 2011
Christon 2010
  • Christon LM, Mackintosh VH, Myers BJ. Use of complementary and alternative medicine (CAM) treatments by parents of children with autism spectrum disorders. Research in Autism Spectrum Disorders 2010;4(2):249-59.
Cohen 1976
  • Cohen DJ, Johnson WT, Caparulo BK. Pica and elevated blood lead level in autistic and atypical children. American Journal of Diseases of Children 1976;130(1):47-8.
Cohen 1982
  • Cohen DJ, Paul R, Anderson GM, Harcherik DF. Blood lead in autistic children. Lancet 1982;320(8289):94-5.
Counter 2004
Dans 2002
  • Dans AL, Tan FN, Villarruz-Sulit EC. Chelation therapy for atherosclerotic cardiovascular disease. Cochrane Database of Systematic Reviews 2002, Issue 4. [DOI: 10.1002/14651858.CD002785]
Deth 2008
Drugs.com 2012
  • Drug Information Online. www.drugs.com/drug-class/chelating-agents.html (accessed 10 January 2012).
Egger 1997
Elbourne 2002
  • Elbourne DR, Altman DG, Higgins JP, Curtin F, Worthington HV, Vail A. Meta-analysis involving cross-over trials: methodological issues. International Journal of Epidemiology 2002;31(1):140-9.
Ernst 2000
Garrecht 2011
  • Garrecht M, Austin DW. The plausibility of a role for mercury in the etiology of autism: a cellular perspective. Toxicological and Environmental Chemistry 2011;93(5-6):1251-73.
Goin-Kochel 2009
  • Goin-Kochel RP, Mackintosh VH, Myers BJ. Parental reports on the efficacy of treatments and therapies for their children with autism spectrum disorders. Research in Autism Spectrum Disorders 2009;3(2):528-37.
Gold 2006
Goldman 2001
  • Goldman LR, Shannon MW, American Academy of Pediatrics: Committee on Environmental Health. Technical report: mercury in the environment: implications for pediatricians. Pediatrics 2001;108(1):197-205.
Golnik 2009
Grandjean 1997
  • Grandjean P, Weihe P, White RF, Debes F, Araki S, Yokoyama K, et al. Cognitive deficit in 7-year-old children with prenatal exposure to methylmercury. Neurotoxicology and Teratology 1997;19(6):417-28.
Green 2006
  • Green VA, Pituch KA, Itchon J, Choi A, O'Reilly M, Sigafoos J. Internet survey of treatments used by parents of children with autism. Research in Developmental Disabilities 2006;27(1):70-84.
Hallmayer 2011
  • Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, et al. Genetic heritability and shared environmental factors among twin pairs with autism. Archives of General Psychiatry 2011;68(11):1095-102.
Hanson 2007
  • Hanson E, Kalish L, Bunce E, Curtis C, McDaniel S, Ware J, et al. Use of complementary and alternative medicine among children diagnosed with autism spectrum disorder. Journal of Autism and Developmental Disorders 2007;37(4):629-36.
Hedge 2009
  • Hedge ML, Bharathi P, Suram A, Venugopal C, Jagannathan R, Poddar P, et al. Challenges associated with metal chelation therapy in Alzheimer's disease. Journal of Alzheimer's Disease 2009;17(3):457-68.
Hertz-Picciotto 2010
  • Hertz-Picciotto I, Green PG, Delwiche L, Hanson R, Walker C, Pessah LN. Blood mercury concentrations in CHARGE Study children with and without autism. Environmental Health Perspectives 2010;118(1):161-6.
Higgins 2011
  • Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.cochrane-handbook.org.
Holmes 2003
James 2011
Kern 2007
Lecavalier 2006
  • Lecavalier L. Behavioral and emotional problems in young people with pervasive developmental disorders: relative prevalence, effects of subject characteristics, and empirical classification. Journal of Autism and Developmental Disorders 2006;36(8):1101-14.
Levy 2003
Liu 2008
  • Liu J, Goyer RA, Waalkes MP. Toxic effects of metals. In: Klaassen CD editor(s). Casarett & Doull's Toxicology: The Basic Science of Poisons. 7th Edition. New York: McGraw-Hill, 2008:931-79.
Millward 2008
Nye 2005
Osterloh 1986
Rahbar 2013
  • Rahbar MH, Samms-Vaughan M, Loveland KA, Ardjomand-Hessabi M, Chen Z, Bressler J, et al. Seafood consumption and blood and blood mercury concentrations in Jamaican children with and without autism spectrum disorders. Neurotoxicity Research 2013;23(1):22-38.
Review Manager 2012
  • The Nordic Cochrane Centre, The Cochrane Collaboration. Review Manager (RevMan). 5.2.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2012.
Semple 2011
  • Semple S, Hewton C, Paterson F, Angley M. Complementary medicine products used in autism - evidence for efficacy and safety. In: Williams T editor(s). Autism Spectrum Disorders - From Genes to Environment. InTech, 2011:77-100.
Shattuck 2007
  • Shattuck PT, Seltzer MM, Greenberg JS, Orsmond GI, Bolt D, Kring S, et al. Change in autism symptoms and maladaptive behaviors in adolescents and adults with an autism spectrum disorder. Journal of Autism and Developmental Disorders 2007;37(9):1735-47.
Sinha 2006
Sinha 2011
Vamnes 2000
  • Vamnes JS, Eide R, Isrenn R, Hol PJ, Gjerdet NR. Diagnostic value of a chelating agent in patients with symptoms allegedly caused by amalgam fillings. Journal of Dental Research 2000;79(3):868-74.
Zecavati 2009