Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2023 Jan 1;180(1):23-40.
doi: 10.1176/appi.ajp.21100992. Epub 2022 Dec 7.

In Search of Biomarkers to Guide Interventions in Autism Spectrum Disorder: A Systematic Review

Mara Parellada  1 , Álvaro Andreu-Bernabeu  1 , Mónica Burdeus  1 , Antonia San José Cáceres  1 , Elena Urbiola  1 , Linda L Carpenter  1 , Nina V Kraguljac  1 , William M McDonald  1 , Charles B Nemeroff  1 , Carolyn I Rodriguez  1 , Alik S Widge  1 , Matthew W State  1 , Stephan J Sanders  1
Affiliations
Meta-Analysis

In Search of Biomarkers to Guide Interventions in Autism Spectrum Disorder: A Systematic Review

Mara Parellada et al. Am J Psychiatry. .

Abstract

Objective: The aim of this study was to catalog and evaluate response biomarkers correlated with autism spectrum disorder (ASD) symptoms to improve clinical trials.

Methods: A systematic review of MEDLINE, Embase, and Scopus was conducted in April 2020. Seven criteria were applied to focus on original research that includes quantifiable response biomarkers measured alongside ASD symptoms. Interventional studies or human studies that assessed the correlation between biomarkers and ASD-related behavioral measures were included.

Results: A total of 5,799 independent records yielded 280 articles for review that reported on 940 biomarkers, 755 of which were unique to a single publication. Molecular biomarkers were the most frequently assayed, including cytokines, growth factors, measures of oxidative stress, neurotransmitters, and hormones, followed by neurophysiology (e.g., EEG and eye tracking), neuroimaging (e.g., functional MRI), and other physiological measures. Studies were highly heterogeneous, including in phenotypes, demographic characteristics, tissues assayed, and methods for biomarker detection. With a median total sample size of 64, almost all of the reviewed studies were only powered to identify biomarkers with large effect sizes. Reporting of individual-level values and summary statistics was inconsistent, hampering mega- and meta-analysis. Biomarkers assayed in multiple studies yielded mostly inconsistent results, revealing a "replication crisis."

Conclusions: There is currently no response biomarker with sufficient evidence to inform ASD clinical trials. This review highlights methodological imperatives for ASD biomarker research necessary to make definitive progress: consistent experimental design, correction for multiple comparisons, formal replication, sharing of sample-level data, and preregistration of study designs. Systematic "big data" analyses of multiple potential biomarkers could accelerate discovery.

Keywords: Autism Spectrum Disorder; Biological Markers; Clinical Drug Studies; Clinical Trials; Endophenotypes; Pharmacodynamic Biomarkers; Response Biomarkers; Symptom Severity; Translational Research.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest

MP has served in advisory or consultancy role for Exeltis and Servier and she is involved in clinical trials promoted by Servier and for F. Hoffmann La Roche Ltd. ASJC has served in an advisory or consultancy role for F. Hoffmann La Roche Ltd and she is involved as consultant in clinical trials conducted by Servier. AA, EU and MB have nothing to declare. LLC has received consulting income from Neuronetics, AffectNeuro, Neurolief, Janssen, Sage Therapeutics, Sunovion, and Otsuka. She has research support from Neuronetics, AffectNeuro, Janssen, and Nexstim. NVK has served as a consultant for Neurocrine Biosicences, Inc. WMM is a member of the American Psychiatric Association (APA) Council on Research representing ECT and Neuromodulation Therapies. He is compensated as the chair of the DSMB for the NIA multicenter study. He is on the Board of Skyland Trail and 3Keys. He is a paid consultant for Signant Health and Sage Therapeutics. He has received past funding from the Stanley Foundation, Soterix, Neuronetics, NeoSync and Cervel Neurotherapeutics. He has endowed chair funded by the JB Fuqua Foundation. He is an employee of Emory University School of Medicine. CBN has served as a consultant for ANeuroTech (division of Anima BV), Signant Health, Sunovion Pharmaceuticals, Inc., Janssen Research & Development LLC, Magstim, Inc., Navitor Pharmaceuticals, Inc., Intra-Cellular Therapies, Inc., EMA Wellness, Acadia Pharmaceuticals, Axsome, Sage, BioXcel Therapeutics, Silo Pharma, XW Pharma, Neuritek, Engrail Therapeutics, Corcept Therapeutics Pharmaceuticals Company. He is a stockholder of Xhale, Seattle Genetics, Antares, BI Gen Holdings, Inc., Corcept Therapeutics Pharmaceuticals Company, EMA Wellness. He serves on the Scientific Advisory Boards of ANeuroTech (division of Anima BV), Brain and Behavior Research Foundation (BBRF), Anxiety and Depression Association of America (ADAA), Skyland Trail, Signant Health, Laureate Institute for Brain Research (LIBR), Inc., Magnolia CNS, Heading Health. He serves on the Board of Directors for Gratitude America, ADAA, Xhale Smart, Inc. He holds patents for “Method and devices for transdermal delivery of lithium” (US 6,375,990B1) and “Method of assessing antidepressant drug therapy via transport inhibition of monoamine neurotransmitters by ex vivo assay” (US 7,148,027B2). CIR has served as a consultant for Epiodyne, received research grant support from Biohaven Pharmaceuticals, and a stipend from APA Publishing for her role as Deputy Editor at The American Journal of Psychiatry. ASW has served as a consultant for Livanova and Dandelion Science. He receives device donations for Medtronic. He has both granted and pending patents related to subject matter discussed in the article. SJS receives research funding from BioMarin Pharmaceutical Inc.

Figures

Figure 1.
Figure 1.. Overview of database search.
A) 5,799 independent records were identified from searches of Medline, Embase, and Scopus. Applying seven inclusion criteria in three steps yielded 248 articles, with an additional 32 included from citations giving a final review set of 280 articles. B) At each step of the application of inclusion criteria the overlap between the three databases is shown by Venn diagrams. C-D) The number of records that met or did not meet combinations of the first three criteria (C) or fifth to seventh criteria (D) is shown in these upset plots. A complete record of this process is recorded in Table S1. E) The 280 articles are subdivided by experimental design. F) Types of biomarker assessed in each of the five types of experimental design.
Figure 2.
Figure 2.. Sample sizes of biomarker studies alongside statistical power calculations.
A) Total sample size (x-axis, logarithmic scale) was calculated by adding all cases and controls in each study. Studies are divided by design (Fig. 1), with a boxplot showing the median sample size (thick black line and vertical dashed lines), the interquartile range (filled box), and data points within 1.5 interquartile ranges (whiskers). Each point of the swarmplot below the corresponding boxplot shows the total sample size of one of the 259 papers reviewed that included human subjects. B) Curves showing the statistical power against total sample size using a two-sided t-test with equal numbers of cases and controls (ratio = 1) at nominal significance (α = 0.05) for four effect sizes: Dramatic (Cohen’s d = 2), Large (Cohen’s d = 0.8), Moderate (Cohen’s d = 0.5), and Small (Cohen’s d = 0.2). C) The analysis in ‘B’ is repeated correcting for all 940 biomarkers with the Bonferroni method (α = 5.3 x 10−5).
Figure 3.
Figure 3.. Molecular biomarkers.
A) The 846 unique molecular biomarkers are represented as nodes (circles) with sizes corresponding to the number of articles in which they are assayed and color representing functional category. Edges (lines) represent co-analysis of the two biomarkers in the same article and the network is displayed with a force-directed layout. B) When reduced glutathione (GSH) is oxidized it forms a dimer called oxidized glutathione (GSSG). C) The direction of effect (color and location) and total sample size (circle size) is shown for six frequently assayed biomarkers in non-interventional studies; each circle represents one study (Table 1). D) The effect size and 95% confidence interval for the difference in glutathione between ASD cases and controls. A positive effect size represents a higher concentration in cases. E) The effect size and 95% confidence interval for the difference in glutathione between treated ASD cases and untreated ASD cases. A positive effect size represents a higher concentration in those receiving the treatment. Hedges’ g* is similar to Cohen’s d, using the same scale but with a correction for small sample sizes (Supplementary methods). Abbreviations: HCY: Homocysteine, GABA: Gamma-aminobutyric acid, MDA: Malondialdehyde, SOD: Superoxide dismutase, TBARS: Thiobarbituric acid-reactive substances, CAT: Catalase, BDNF: Brain-Derived Neurotrophic Factor, TNFα: Tumor Necrosis Factor, GM-CSF: Granulocyte-Macrophage Colony Stimulating Factor (CSF2), CCL2: C-C motif chemokine ligand 2 (MCP-1), CCL3: C-C motif chemokine ligand 3 (MIP-1α), CCL4: C-C motif chemokine ligand 4 (MIP-1-beta), CCL5: C-C motif chemokine ligand 5 (RANTES), IFN-γ: Interferon gamma (IFNG), CRP: C-reactive protein, IL: interleukin, CXCL10: C-X-C motif chemokine ligand 10 (IP-10).
Figure 4.
Figure 4.. Non-molecular biomarkers.
A) The 94 unique non-molecular biomarkers are represented as nodes (circles) with size corresponding to the number of articles in which they are assayed and color representing biomarker category. Edges (lines) represent co-analysis of the two biomarkers in the same article and the network is displayed with a force-directed layout. B) Distribution of the total number of papers split by modality. C) The direction of effect (location and color) and total sample size (circle size) is shown for six most frequently assayed biomarkers; each circle represents one study (Table 1). Abbreviations: CNS: Central nervous system; DTI: Diffusion tensor imaging; EEG: electroencephalography; MEG: magnetoencephalography.
See this image and copyright information in PMC

References

    1. Association AP. Diagnostic and statistical manual of mental disorders, 5th Edition (DSM-V): American Psychiatric Publishing, Washington, DC; 2013.
    1. Maenner MJ, Shaw KA, Baio J. Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveillance Summaries. 2020;69(4):1. - PMC - PubMed
    1. Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5(6):463–6. - PMC - PubMed
    1. Group F-NBW. BEST (Biomarkers, EndpointS, and other Tools) resource. 2016. - PubMed
    1. Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood). 2018;243(3):213–21. - PMC - PubMed

Publication types