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@Article{ 10.1001/jamapsychiatry.2019.1956,
author = {Rødgaard, Eya-Mist and Jensen, Kristian and Vergnes,
Jean-Noël and Soulières, Isabelle and Mottron, Laurent},
title = "{Temporal Changes in Effect Sizes of Studies Comparing
Individuals With and Without Autism: A Meta-analysis}",
journal = {JAMA Psychiatry},
volume = {76},
number = {11},
pages = {1124-1132},
year = {2019},
month = {11},
abstract = "{The definition and nature of autism have been highly
debated, as exemplified by several revisions of the DSM
(DSM-III, DSM-IIIR, DSM-IV, and DSM-5) criteria. There has
recently been a move from a categorical view toward a
spectrum-based view. These changes have been accompanied by
a steady increase in the prevalence of the condition.
Changes in the definition of autism that may increase
heterogeneity could affect the results of autism research;
specifically, a broadening of the population with autism
could result in decreasing effect sizes of group comparison
studies.To examine the correlation between publication year
and effect size of autism-control group comparisons across
several domains of published autism neurocognitive
research.This meta-analysis investigated 11 meta-analyses
obtained through a systematic search of PubMed for
meta-analyses published from January 1, 1966, through
January 27, 2019, using the search string autism AND
(meta-analysis OR meta-analytic). The last search was
conducted on January 27, 2019.Meta-analyses were included
if they tested the significance of group differences
between individuals with autism and control individuals on
a neurocognitive construct. Meta-analyses were only
included if the tested group difference was significant and
included data with a span of at least 15 years.Data were
extracted and analyzed according to the Preferred Reporting
Items for Systematic Reviews and Meta-analyses (PRISMA)
reporting guideline using fixed-effects models.Estimated
slope of the correlation between publication year and
effect size, controlling for differences in methods, sample
size, and study quality.The 11 meta-analyses included data
from a total of 27 723 individuals. Demographic data such
as sex and age were not available for the entire data set.
Seven different psychological and neurologic constructs
were analyzed based on data from these meta-analyses.
Downward temporal trends for effect size were found for all
constructs (slopes: –0.067 to –0.003), with the trend
being significant in 5 of 7 cases: emotion recognition
(slope: –0.028 [95\% CI, –0.048 to –0.007]), theory
of mind (–0.045 [95\% CI, –0.066 to –0.024]),
planning (–0.067 [95\% CI, –0.125 to –0.009]), P3b
amplitude (–0.048 [95\% CI, –0.093 to –0.004]), and
brain size (–0.047 [95\% CI, –0.077 to –0.016]). In
contrast, 3 analogous constructs in schizophrenia, a
condition that is also heterogeneous but with no reported
increase in prevalence, did not show a similar trend.The
findings suggest that differences between individuals with
autism and those without the diagnosis have decreased over
time and that possible changes in the definition of autism
from a narrowly defined and homogenous population toward an
inclusive and heterogeneous population may reduce our
capacity to build mechanistic models of the condition.}",
issn = {2168-622X},
doi = {10.1001/jamapsychiatry.2019.1956},
url = {https://doi.org/10.1001/jamapsychiatry.2019.1956}
}
@Article{ abbas2020multi,
title = {Multi-modular Ai Approach to Streamline Autism Diagnosis
in Young children},
author = {Abbas, Halim and Garberson, Ford and Liu-Mayo, Stuart and
Glover, Eric and Wall, Dennis P},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {1--8},
year = {2020},
publisher = {Nature Publishing Group}
}
@Article{ althouse2006pediatric,
title = {Pediatric workforce: A look at pediatric nephrology data
from the American Board of Pediatrics},
author = {Althouse, Linda A and Stockman, James A},
journal = {The Journal of pediatrics},
volume = {148},
number = {5},
pages = {575--576},
year = {2006},
publisher = {Elsevier}
}
@Article{ bisgaier2011access,
title = {Access to autism evaluation appointments with
developmental-behavioral and neurodevelopmental
subspecialists},
author = {Bisgaier, Joanna and Levinson, Dana and Cutts, Diana B and
Rhodes, Karin V},
journal = {Archives of pediatrics \& adolescent medicine},
volume = {165},
number = {7},
pages = {673--674},
year = {2011},
publisher = {American Medical Association}
}
@Article{ bishop2018using,
title = {Using machine learning to identify patterns of lifetime
health problems in decedents with autism spectrum
disorder},
author = {Bishop-Fitzpatrick, Lauren and Movaghar, Arezoo and
Greenberg, Jan S and Page, David and DaWalt, Leann S and
Brilliant, Murray H and Mailick, Marsha R},
journal = {Autism Research},
volume = {11},
number = {8},
pages = {1120--1128},
year = {2018},
publisher = {Wiley Online Library}
}
@Article{ bondy2008graph,
title = {Graph theory (2008)},
author = {Bondy, JA and Murty, USR},
journal = {Grad. Texts in Math},
year = {2008}
}
@Misc{ cdc,
title = {Data \& Statistics on Autism Spectrum Disorder | CDC},
url = {https://www.cdc.gov/ncbddd/autism/data.html},
journal = {Centers for Disease Control and Prevention},
publisher = {Centers for Disease Control and Prevention},
year = {2019},
month = {Apr}
}
@Misc{ cdc0,
title = {Data \& Statistics on Autism Spectrum Disorder | CDC},
url = {https://www.cdc.gov/ncbddd/autism},
journal = {Centers for Disease Control and Prevention},
author = {\textrm{Centers for Disease Control and Prevention}},
year = {2019},
month = {Apr}
}
@Misc{ cdccp,
title = {Prevalence of cerebral palsy, co-occurring autism spectrum
disorders, and motor functioning},
url = {https://www.cdc.gov/ncbddd/cp/features/prevalence.html},
journal = {Centers for Disease Control \& Prevention},
author = {\textrm{Centers for Disease Control and Prevention}},
year = {2020},
month = {Apr}
}
@Article{ cl12g,
author = "Chattopadhyay, I. and Lipson, H. ",
title = "{{A}bductive learning of quantized stochastic processes
with probabilistic finite automata}",
journal = "Philos Trans A",
year = "2013",
volume = "371",
number = "1984",
pages = "20110543",
month = "Feb"
}
@Article{ chattopadhyay2008structural,
title = {Structural transformations of probabilistic finite state
machines},
author = {Chattopadhyay, Ishanu and Ray, Asok},
journal = {International Journal of Control},
volume = {81},
number = {5},
pages = {820--835},
year = {2008},
publisher = {Taylor \& Francis}
}
@Article{ chattopadhyay2014data,
title = {Data smashing: uncovering lurking order in data},
author = {Chattopadhyay, Ishanu and Lipson, Hod},
journal = {Journal of The Royal Society Interface},
volume = {11},
number = {101},
pages = {20140826},
year = {2014},
publisher = {Toe Royal Society}
}
@Article{ christensen2014prevalence,
title = {Prevalence of cerebral palsy, co-occurring autism spectrum
disorders, and motor functioning--A utism and D
evelopmental D isabilities M onitoring N etwork, USA,
2008},
author = {Christensen, Deborah and Van Naarden Braun, Kim and
Doernberg, Nancy S and Maenner, Matthew J and Arneson,
Carrie L and Durkin, Maureen S and Benedict, Ruth E and
Kirby, Russell S and Wingate, Martha S and Fitzgerald,
Robert and others},
journal = {Developmental Medicine \& Child Neurology},
volume = {56},
number = {1},
pages = {59--65},
year = {2014},
publisher = {Wiley Online Library}
}
@Book{ cover,
author = {Cover, Thomas M. and Thomas, Joy A.},
title = {Elements of Information Theory (Wiley Series in
Telecommunications and Signal Processing)},
year = {2006},
isbn = {0471241954},
publisher = {Wiley-Interscience},
address = {New York, NY, USA}
}
@Book{ cover2012elements,
title = {Elements of information theory},
author = {Cover, Thomas M and Thomas, Joy A},
year = {2012},
publisher = {John Wiley \& Sons}
}
@Book{ doob1953stochastic,
title = {Stochastic Processes},
author = {Doob, J.L.},
isbn = {9780471218135},
lccn = {lc52011857},
series = {Wiley Publications in Statistics},
year = {1953},
publisher = {John Wiley \& Sons}
}
@Book{ doob1990stochastic,
title = {Stochastic processes},
author = {Doob, J.L.},
lccn = {52011857},
series = {Wiley publications in statistics},
year = {1990},
publisher = {Wiley}
}
@Article{ doshi2014comorbidity,
title = {Comorbidity clusters in autism spectrum disorders: an
electronic health record time-series analysis},
author = {Doshi-Velez, Finale and Ge, Yaorong and Kohane, Isaac},
journal = {Pediatrics},
volume = {133},
number = {1},
pages = {e54--e63},
year = {2014},
publisher = {Am Acad Pediatrics}
}
@Article{ duda2014testing,
title = {Testing the accuracy of an observation-based classifier
for rapid detection of autism risk},
author = {Duda, M and Kosmicki, JA and Wall, DP},
journal = {Translational psychiatry},
volume = {4},
number = {8},
pages = {e424--e424},
year = {2014},
publisher = {Nature Publishing Group}
}
@Article{ duda2016clinical,
title = {Clinical evaluation of a novel and mobile autism risk
assessment},
author = {Duda, Marlena and Daniels, Jena and Wall, Dennis P},
journal = {Journal of autism and developmental disorders},
volume = {46},
number = {6},
pages = {1953--1961},
year = {2016},
publisher = {Springer}
}
@Article{ fenikile2015barriers,
title = {Barriers to autism screening in family medicine practice:
a qualitative study},
author = {Fenikil{\'e}, Tsehaiwork Sunny and Ellerbeck, Kathryn and
Filippi, Melissa K and Daley, Christine M},
journal = {Primary health care research \& development},
volume = {16},
number = {4},
pages = {356--366},
year = {2015},
publisher = {Cambridge University Press}
}
@Article{ fusaro2014potential,
title = {The potential of accelerating early detection of autism
through content analysis of YouTube videos},
author = {Fusaro, Vincent A and Daniels, Jena and Duda, Marlena and
DeLuca, Todd F and D’Angelo, Olivia and Tamburello, Jenna
and Maniscalco, James and Wall, Dennis P},
journal = {PLOS one},
volume = {9},
number = {4},
pages = {e93533},
year = {2014},
publisher = {Public Library of Science}
}
@Article{ gordon2016whittling,
title = {Whittling down the wait time: exploring models to minimize
the delay from initial concern to diagnosis and treatment
of autism spectrum disorder},
author = {Gordon-Lipkin, Eliza and Foster, Jessica and Peacock,
Georgina},
journal = {Pediatric Clinics},
volume = {63},
number = {5},
pages = {851--859},
year = {2016},
publisher = {Elsevier}
}
@Article{ hansen2017truven,
title = {The Truven health MarketScan databases for life sciences
researchers},
author = {Hansen, L},
journal = {Truven Health Ananlytics IBM Watson Health},
year = {2017}
}
@Article{ hardy1992divergent,
title = {Divergent series, With a preface by JE Littlewood and a
note by LS Bosanquet, Reprint of the revised (1963)
edition},
author = {Hardy, GH},
journal = {{\'E}ditions Jacques Gabay, Sceaux},
year = {1992}
}
@Article{ hicks2018validation,
title = {Validation of a salivary RNA test for childhood autism
spectrum disorder},
author = {Hicks, Steven D and Rajan, Alexander T and Wagner, Kayla E
and Barns, Sarah and Carpenter, Randall L and Middleton,
Frank A},
journal = {Frontiers in genetics},
volume = {9},
pages = {534},
year = {2018},
publisher = {Frontiers}
}
@Book{ hopcroft2008introduction,
title = {Introduction to automata theory, languages, and
computation},
author = {Hopcroft, John E},
year = {2008},
publisher = {Pearson Education India}
}
@Article{ howsmon2017classification,
title = {Classification and adaptive behavior prediction of
children with autism spectrum disorder based upon
multivariate data analysis of markers of oxidative stress
and DNA methylation},
author = {Howsmon, Daniel P and Kruger, Uwe and Melnyk, Stepan and
James, S Jill and Hahn, Juergen},
journal = {PLoS computational biology},
volume = {13},
number = {3},
pages = {e1005385},
year = {2017},
publisher = {Public Library of Science San Francisco, CA USA}
}
@Article{ hyde2019applications,
title = {Applications of supervised machine learning in autism
spectrum disorder research: a review},
author = {Hyde, Kayleigh K and Novack, Marlena N and LaHaye,
Nicholas and Parlett-Pelleriti, Chelsea and Anden, Raymond
and Dixon, Dennis R and Linstead, Erik},
journal = {Review Journal of Autism and Developmental Disorders},
volume = {6},
number = {2},
pages = {128--146},
year = {2019},
publisher = {Springer}
}
@Article{ hyman2020identification,
title = {Identification, Evaluation, and Management of Children
With Autism Spectrum Disorder},
author = {Hyman, Susan L and Levy, Susan E and Myers, Scott M and
others},
journal = {Pediatrics},
volume = {145},
number = {1},
year = {2020},
publisher = {Am Acad Pediatrics}
}
@Book{ kai1967markov_stdis,
title = {Markov Chains: With Stationary Transition Probabilities},
author = {Kai, Lai Chung},
year = {1967},
publisher = {Springer-Verlag}
}
@Article{ kalb2012determinants,
title = {Determinants of appointment absenteeism at an outpatient
pediatric autism clinic},
author = {Kalb, Luther G and Freedman, Brian and Foster, Catherine
and Menon, Deepa and Landa, Rebecca and Kishfy, Louis and
Law, Paul},
journal = {Journal of Developmental \& Behavioral Pediatrics},
volume = {33},
number = {9},
pages = {685--697},
year = {2012},
publisher = {LWW}
}
@Book{ klenke2013probability,
title = {Probability theory: a comprehensive course},
author = {Klenke, Achim},
year = {2013},
publisher = {Springer Science \& Business Media}
}
@Article{ kullback1951,
author = "Kullback, S. and Leibler, R. A.",
doi = "10.1214/aoms/1177729694",
fjournal = "The Annals of Mathematical Statistics",
journal = "Ann. Math. Statist.",
month = "03",
number = "1",
pages = "79--86",
publisher = "The Institute of Mathematical Statistics",
title = "On Information and Sufficiency",
volume = "22",
year = "1951"
}
@Article{ li2018high,
title = {High efficiency classification of children with autism
spectrum disorder},
author = {Li, Genyuan and Lee, Olivia and Rabitz, Herschel},
journal = {PloS one},
volume = {13},
number = {2},
pages = {e0192867},
year = {2018},
publisher = {Public Library of Science San Francisco, CA USA}
}
@Article{ lingren2016electronic,
title = {Electronic health record based algorithm to identify
patients with autism spectrum disorder},
author = {Lingren, Todd and Chen, Pei and Bochenek, Joseph and
Doshi-Velez, Finale and Manning-Courtney, Patty and Bickel,
Julie and Wildenger Welchons, Leah and Reinhold, Judy and
Bing, Nicole and Ni, Yizhao and others},
journal = {PloS one},
volume = {11},
number = {7},
pages = {e0159621},
year = {2016},
publisher = {Public Library of Science San Francisco, CA USA}
}
@Article{ matthews2016sparse,
title = {On sparse variational methods and the Kullback-Leibler
divergence between stochastic processes},
author = {Matthews, Alexander G de G and Hensman, James and Turner,
Richard and Ghahramani, Zoubin},
journal = {Journal of Machine Learning Research},
volume = {51},
pages = {231--239},
year = {2016}
}
@Misc{ nimh,
title = {Early Screening for Autism Spectrum},
url = {https://www.nimh.nih.gov/},
author = {Lisa Gilotty},
publisher = {National Institute of Mental Health},
year = {2019},
month = {Apr}
}
@Article{ pearce2000,
abstract = {When I first started studying epidemiology, ecological
studies were briefly discussed as an inexpensive but
unreliable method for studying individual level risk
factors for disease. For example, rather than go to the
time and expense to establish a cohort study or
case-control study of fat intake and breast cancer, you
could simply use national dietary and cancer incidence data
and, with minimal time and expense, show a strong
correlation internationally between fat intake and breast
cancer. This approach was quite rightly regarded as
inadequate and unreliable because of the many additional
forms of bias that can occur in such studies compared with
studies of individuals within a population. In particular,
the “ecological fallacy” can occur in that factors that
are associated with national disease rates may not be
associated with disease in individuals.1 For example,
almost any disease that is associated with affluence and
Westernisation has in the past been associated at the
national level with sales of television sets, and nowadays
is probably associated at the national level with rates of
internet use.
Thus, ecological studies were not a good thing to do, and
were a relic of the “pre-modern” phase of epidemiology
before it became firmly established with a methodologic
paradigm based on the theory of randomised controlled
trials of individuals. This paradigm, which is very
powerful when used appropriately, gave rise to increasingly
sophisticated methods of study design and data analysis. In
particular, biostatistical methods that were developed for
randomised trials involving a single individual level
exposure were used to reformulate and make more rigorous
the previously ad hoc epidemiological methods of study
design and data analysis.2 3 Thus, epidemiology courses
have increasingly become restricted to discussing cohort
and case-control studies and the methods of data analysis
that fit the clinical trial paradigm on which {\ldots}},
author = {Pearce, N},
doi = {10.1136/JECH.54.5.326},
file = {:home/ishanu/Documents/Mendeley Desktop/Pearce/Journal of
epidemiology and community health/Pearce - 2000 - The
ecological fallacy strikes back.pdf:pdf},
issn = {0143-005X},
journal = {Journal of epidemiology and community health},
mendeley-groups={Information{\_}bottleneck,Genome{\_}complexity},
month = {may},
number = {5},
pages = {326--7},
pmid = {10814650},
publisher = {BMJ Publishing Group Ltd},
title = {{The ecological fallacy strikes back.}},
volume = {54},
year = {2000}
}
@Article{ pmid15546155,
author = "Vargas, D. L. and Nascimbene, C. and Krishnan, C. and
Zimmerman, A. W. and Pardo, C. A. ",
title = "{{N}euroglial activation and neuroinflammation in the
brain of patients with autism}",
journal = "Ann. Neurol.",
year = "2005",
volume = "57",
number = "1",
pages = "67--81",
month = "Jan"
}
@Article{ pmid21282636,
author = "Diaz Heijtz, R. and Wang, S. and Anuar, F. and Qian, Y.
and Bjorkholm, B. and Samuelsson, A. and Hibberd, M. L. and
Forssberg, H. and Pettersson, S. ",
title = "{{N}ormal gut microbiota modulates brain development and
behavior}",
journal = "Proc. Natl. Acad. Sci. U.S.A.",
year = "2011",
volume = "108",
number = "7",
pages = "3047--3052",
month = "Feb",
abstract = {Microbial colonization of mammals is an evolution-driven
process that modulate host physiology, many of which are
associated with immunity and nutrient intake. Here, we
report that colonization by gut microbiota impacts
mammalian brain development and subsequent adult behavior.
Using measures of motor activity and anxiety-like behavior,
we demonstrate that germ free (GF) mice display increased
motor activity and reduced anxiety, compared with specific
pathogen free (SPF) mice with a normal gut microbiota. This
behavioral phenotype is associated with altered expression
of genes known to be involved in second messenger pathways
and synaptic long-term potentiation in brain regions
implicated in motor control and anxiety-like behavior. GF
mice exposed to gut microbiota early in life display
similar characteristics as SPF mice, including reduced
expression of PSD-95 and synaptophysin in the striatum.
Hence, our results suggest that the microbial colonization
process initiates signaling mechanisms that affect neuronal
circuits involved in motor control and anxiety behavior.}
}
@Article{ pmid21595886,
author = "Wei, H. and Zou, H. and Sheikh, A. M. and Malik, M. and
Dobkin, C. and Brown, W. T. and Li, X. ",
title = "{{I}{L}-6 is increased in the cerebellum of autistic brain
and alters neural cell adhesion, migration and synaptic
formation}",
journal = "J Neuroinflammation",
year = "2011",
volume = "8",
pages = "52",
month = "May"
}
@Article{ pmid21629840,
author = "Young, A. M. and Campbell, E. and Lynch, S. and Suckling,
J. and Powis, S. J. ",
title = "{{A}berrant {N}{F}-kappa{B} expression in autism spectrum
condition: a mechanism for neuroinflammation}",
journal = "Front Psychiatry",
year = "2011",
volume = "2",
pages = "27"
}
@Article{ pmid21651783,
author = "Adams, J. B. and Audhya, T. and McDonough-Means, S. and
Rubin, R. A. and Quig, D. and Geis, E. and Gehn, E. and
Loresto, M. and Mitchell, J. and Atwood, S. and Barnhouse,
S. and Lee, W. ",
title = "{{N}utritional and metabolic status of children with
autism vs. neurotypical children, and the association with
autism severity}",
journal = "Nutr Metab (Lond)",
year = "2011",
volume = "8",
number = "1",
pages = "34",
month = "Jun",
abstract = {The relationship between relative metabolic disturbances
and developmental disorders is an emerging research focus.
This study compares the nutritional and metabolic status of
children with autism with that of neurotypical children and
investigates the possible association of autism severity
with biomarkers.\\ Participants were children ages 5-16
years in Arizona with Autistic Spectrum Disorder (n = 55)
compared with non-sibling, neurotypical controls (n = 44)
of similar age, gender and geographical distribution.
Neither group had taken any vitamin/mineral supplements in
the two months prior to sample collection. Autism severity
was assessed using the Pervasive Development Disorder
Behavior Inventory (PDD-BI), Autism Treatment Evaluation
Checklist (ATEC), and Severity of Autism Scale (SAS). Study
measurements included: vitamins, biomarkers of vitamin
status, minerals, plasma amino acids, plasma glutathione,
and biomarkers of oxidative stress, methylation, sulfation
and energy production.\\ Biomarkers of children with autism
compared to those of controls using a t-test or Wilcoxon
test found the following statistically significant
differences (p < 0.001): Low levels of biotin, plasma
glutathione, RBC SAM, plasma uridine, plasma ATP, RBC NADH,
RBC NADPH, plasma sulfate (free and total), and plasma
tryptophan; also high levels of oxidative stress markers
and plasma glutamate. Levels of biomarkers for the
neurotypical controls were in good agreement with accessed
published reference ranges. In the Autism group, mean
levels of vitamins, minerals, and most amino acids commonly
measured in clinical care were within published reference
ranges.A stepwise, multiple linear regression analysis
demonstrated significant associations between several
groups of biomarkers with all three autism severity scales,
including vitamins (adjusted R2 of 0.25-0.57), minerals
(adj. R2 of 0.22-0.38), and plasma amino acids (adj. R2 of
0.22-0.39).\\ The autism group had many statistically
significant differences in their nutritional and metabolic
status, including biomarkers indicative of vitamin
insufficiency, increased oxidative stress, reduced capacity
for energy transport, sulfation and detoxification. Several
of the biomarker groups were significantly associated with
variations in the severity of autism. These nutritional and
metabolic differences are generally in agreement with other
published results and are likely amenable to nutritional
supplementation. Research investigating treatment and its
relationship to the co-morbidities and etiology of autism
is warranted.}
}
@Article{ pmid22511918,
author = "Kohane, I. S. and McMurry, A. and Weber, G. and MacFadden,
D. and Rappaport, L. and Kunkel, L. and Bickel, J. and
Wattanasin, N. and Spence, S. and Murphy, S. and Churchill,
S. ",
title = "{{T}he co-morbidity burden of children and young adults
with autism spectrum disorders}",
journal = "PLoS ONE",
year = "2012",
volume = "7",
number = "4",
pages = "e33224",
abstract = {Use electronic health records Autism Spectrum Disorder
(ASD) to assess the comorbidity burden of ASD in children
and young adults.\\ A retrospective prevalence study was
performed using a distributed query system across three
general hospitals and one pediatric hospital. Over 14,000
individuals under age 35 with ASD were characterized by
their co-morbidities and conversely, the prevalence of ASD
within these comorbidities was measured. The comorbidity
prevalence of the younger (Age<18 years) and older (Age
18-34 years) individuals with ASD was compared.\\ 19.44\%
of ASD patients had epilepsy as compared to 2.19\% in the
overall hospital population (95\% confidence interval for
difference in percentages 13.58-14.69\%), 2.43\% of ASD
with schizophrenia vs. 0.24\% in the hospital population
(95\% CI 1.89-2.39\%), inflammatory bowel disease (IBD)
0.83\% vs. 0.54\% (95\% CI 0.13-0.43\%), bowel disorders
(without IBD) 11.74\% vs. 4.5\% (95\% CI 5.72-6.68\%),
CNS/cranial anomalies 12.45\% vs. 1.19\% (95\% CI
9.41-10.38\%), diabetes mellitus type I (DM1) 0.79\% vs.
0.34\% (95\% CI 0.3-0.6\%), muscular dystrophy 0.47\% vs
0.05\% (95\% CI 0.26-0.49\%), sleep disorders 1.12\% vs.
0.14\% (95\% CI 0.79-1.14\%). Autoimmune disorders
(excluding DM1 and IBD) were not significantly different at
0.67\% vs. 0.68\% (95\% CI -0.14-0.13\%). Three of the
studied comorbidities increased significantly when
comparing ages 0-17 vs 18-34 with p<0.001: Schizophrenia
(1.43\% vs. 8.76\%), diabetes mellitus type I (0.67\% vs.
2.08\%), IBD (0.68\% vs. 1.99\%) whereas sleeping
disorders, bowel disorders (without IBD) and epilepsy did
not change significantly.\\ The comorbidities of ASD
encompass disease states that are significantly
overrepresented in ASD with respect to even the patient
populations of tertiary health centers. This burden of
comorbidities goes well beyond those routinely managed in
developmental medicine centers and requires broad
multidisciplinary management that payors and providers will have to plan for.}
}
@Article{ pmid23537858,
author = "Murdoch, J. D. and State, M. W. ",
title = "{{R}ecent developments in the genetics of autism spectrum
disorders}",
journal = "Curr. Opin. Genet. Dev.",
year = "2013",
volume = "23",
number = "3",
pages = "310--315",
month = "Jun",
abstract = {The last several years have marked a turning point in the
genetics of autism spectrum disorder (ASD) due to rapidly
advancing genomic technologies. As the pool of bona fide
risk genes and regions accumulates, several key themes have
emerged: these include the important role of rare and de
novo mutation, the biological overlap among so-called
syndromic and 'idiopathic' ASD, the elusive nature of the
common variant contribution to risk, and the observation
that the tremendous locus heterogeneity underlying ASD
appears to converge on a relatively small number of key
biological processes. Perhaps most striking has been the
revelation that ASD mutations show tremendous phenotypic
variability ranging from social disability to
schizophrenia, intellectual disability, language
impairment, epilepsy and typical development.}
}
@Article{ pmid23637569,
author = "Hu, V. W. ",
title = "{{T}he expanding genomic landscape of autism: discovering
the 'forest' beyond the 'trees'}",
journal = "Future Neurol",
year = "2013",
volume = "8",
number = "1",
pages = "29--42",
month = "Jan",
abstract = {Autism spectrum disorders are neurodevelopmental disorders
characterized by significant deficits in reciprocal social
interactions, impaired communication and restricted,
repetitive behaviors. As autism spectrum disorders are
among the most heritable of neuropsychiatric disorders,
much of autism research has focused on the search for
genetic variants in protein-coding genes (i.e., the
'trees'). However, no single gene can account for more than
1\% of the cases of autism spectrum disorders. Yet,
genome-wide association studies have often identified
statistically significant associations of genetic
variations in regions of DNA that do not code for proteins
(i.e., intergenic regions). There is increasing evidence
that such noncoding regions are actively transcribed and
may participate in the regulation of genes, including genes
on different chromosomes. This article summarizes evidence
that suggests that the research spotlight needs to be
expanded to encompass far-reaching gene-regulatory
mechanisms that include a variety of epigenetic
modifications, as well as noncoding RNA (i.e., the
'forest'). Given that noncoding RNA represents over 90\% of
the transcripts in most cells, we may be observing just the
'tip of the iceberg' or the 'edge of the forest' in the
genomic landscape of autism.}
}
@Article{ pmid23935565,
author = "Won, H. and Mah, W. and Kim, E. ",
title = "{{A}utism spectrum disorder causes, mechanisms, and
treatments: focus on neuronal synapses}",
journal = "Front Mol Neurosci",
year = "2013",
volume = "6",
pages = "19"
}
@Article{ pmid24529515,
author = "Schieve, L. A. and Tian, L. H. and Baio, J. and Rankin, K.
and Rosenberg, D. and Wiggins, L. and Maenner, M. J. and
Yeargin-Allsopp, M. and Durkin, M. and Rice, C. and King,
L. and Kirby, R. S. and Wingate, M. S. and Devine, O. ",
title = "{{P}opulation attributable fractions for three perinatal
risk factors for autism spectrum disorders, 2002 and 2008
autism and developmental disabilities monitoring network}",
journal = "Ann Epidemiol",
year = "2014",
volume = "24",
number = "4",
pages = "260--266",
month = "Apr",
abstract = {Numerous studies establish associations between adverse
perinatal outcomes/complications and autism spectrum
disorder (ASD). There has been little assessment of
population attributable fractions (PAFs).\\ We estimated
average ASD PAFs for preterm birth (PTB), small for
gestational age (SGA), and Cesarean delivery (CD) in a U.S.
population. Average PAF methodology accounts for risk
factor co-occurrence. ASD cases were singleton non-Hispanic
white, non-Hispanic black, and Hispanic children born in
1994 (n = 703) or 2000 (n = 1339) who resided in 48 U.S.
counties included within eight Autism and Developmental
Disabilities Monitoring Network sites. Cases were matched
on birth year, sex, and maternal county of residence,
race-ethnicity, age, and education to 20 controls from U.S.
natality files.\\ For the 1994 cohort, average PAFs were
4.2\%, 0.9\%, and 7.9\% for PTB, SGA, and CD, respectively.
The summary PAF was 13.0\% (1.7\%-19.5\%). For the 2000
cohort, average PAFs were 2.0\%, 3.1\%, and 6.7\% for PTB,
SGA, and CD, respectively, with a summary PAF of 11.8\%
(7.5\%-15.9\%).\\ Three perinatal risk factors notably
contribute to ASD risk in a U.S. population. Because each
factor represents multiple etiologic pathways, PAF
estimates are best interpreted as the proportion of ASD
attributable to having a suboptimal perinatal environment
resulting in PTB, SGA, and/or CD.}
}
@Article{ pmid24729779,
author = "Kayser, M. S. and Dalmau, J. ",
title = "{{A}nti-{N}{M}{D}{A} {R}eceptor {E}ncephalitis in
{P}sychiatry}",
journal = "Curr Psychiatry Rev",
year = "2011",
volume = "7",
number = "3",
pages = "189--193"
}
@Article{ pmid25038753,
author = "Gaugler, T. and Klei, L. and Sanders, S. J. and Bodea, C.
A. and Goldberg, A. P. and Lee, A. B. and Mahajan, M. and
Manaa, D. and Pawitan, Y. and Reichert, J. and Ripke, S.
and Sandin, S. and Sklar, P. and Svantesson, O. and
Reichenberg, A. and Hultman, C. M. and Devlin, B. and
Roeder, K. and Buxbaum, J. D. ",
title = "{{M}ost genetic risk for autism resides with common
variation}",
journal = "Nat. Genet.",
year = "2014",
volume = "46",
number = "8",
pages = "881--885",
month = "Aug",
abstract = {A key component of genetic architecture is the allelic
spectrum influencing trait variability. For autism spectrum
disorder (herein termed autism), the nature of the allelic
spectrum is uncertain. Individual risk-associated genes
have been identified from rare variation, especially de
novo mutations. From this evidence, one might conclude that
rare variation dominates the allelic spectrum in autism,
yet recent studies show that common variation, individually
of small effect, has substantial impact en masse. At issue
is how much of an impact relative to rare variation this
common variation has. Using a unique epidemiological sample
from Sweden, new methods that distinguish total
narrow-sense heritability from that due to common variation
and synthesis of results from other studies, we reach
several conclusions about autism's genetic architecture:
its narrow-sense heritability is 52.4\%, with most due to
common variation, and rare de novo mutations contribute
substantially to individual liability, yet their
contribution to variance in liability, 2.6\%, is modest
compared to that for heritable variation.}
}
@Article{ pmid25681541,
author = "Zerbo, O. and Leong, A. and Barcellos, L. and Bernal, P.
and Fireman, B. and Croen, L. A. ",
title = "{{I}mmune mediated conditions in autism spectrum
disorders}",
journal = "Brain Behav. Immun.",
year = "2015",
volume = "46",
pages = "232--236",
month = "May",
abstract = {We conducted a case-control study among members of Kaiser
Permanente Northern California (KPNC) born between 1980 and
2003 to determine the prevalence of immune-mediated
conditions in individuals with autism, investigate whether
these conditions occur more often than expected, and
explore the timing of onset relative to autism diagnosis.
Cases were children and young adults with at least two
autism diagnoses recorded in outpatient records (n=5565).
Controls were children without autism randomly sampled at a
ratio of 5 to 1, matched to cases on birth year, sex, and
length of KPNC membership (n=27,825). The main outcomes -
asthma, allergies, and autoimmune diseases - were
identified from KPNC inpatient and outpatient databases.
Chi-square tests were used to evaluate case-control
differences. Allergies and autoimmune diseases were
diagnosed significantly more often among children with
autism than among controls (allergy: 20.6\% vs. 17.7\%,
Crude odds ratio (OR)=1.22, 95\% confidence interval (CI)
1.13-1.31; autoimmune disease: 1\% vs. 0.76\%, OR=1.36,
95\% CI 1.01-1.83), and asthma was diagnosed significantly
less often (13.7\% vs. 15.9\%; OR=0.83, 95\% CI 0.76-0.90).
Psoriasis occurred more than twice as often in cases than
in controls (0.34\% vs. 0.15\%; OR=2.35, 95\% CI
1.36-4.08). Our results support previous observations that
children with autism have elevated prevalence of specific
immune-related comorbidities.}
}
@Article{ pmid26793298,
author = "Young, A. M. and Chakrabarti, B. and Roberts, D. and Lai,
M. C. and Suckling, J. and Baron-Cohen, S. ",
title = "{{F}rom molecules to neural morphology: understanding
neuroinflammation in autism spectrum condition}",
journal = "Mol Autism",
year = "2016",
volume = "7",
pages = "9"
}
@Article{ pmid27351598,
author = "Theoharides, T. C. and Tsilioni, I. and Patel, A. B. and
Doyle, R. ",
title = "{{A}topic diseases and inflammation of the brain in the
pathogenesis of autism spectrum disorders}",
journal = "Transl Psychiatry",
year = "2016",
volume = "6",
number = "6",
pages = "e844",
month = "06"
}
@Article{ pmid27565363,
author = "Gordon-Lipkin, E. and Foster, J. and Peacock, G. ",
title = "{{W}hittling {D}own the {W}ait {T}ime: {E}xploring
{M}odels to {M}inimize the {D}elay from {I}nitial {C}oncern
to {D}iagnosis and {T}reatment of {A}utism {S}pectrum
{D}isorder}",
journal = "Pediatr. Clin. North Am.",
year = "2016",
volume = "63",
number = "5",
pages = "851--859",
month = "10",
abstract = {The process from initial concerns to diagnosis of autism
spectrum disorder (ASD) can be a long and complicated
process. The traditional model for evaluation and diagnosis
of ASD often consists of long wait-lists and evaluations
that result in a 2-year difference between the earliest
signs of ASD and mean age of diagnosis. Multiple factors
contribute to this diagnostic bottleneck, including
time-consuming evaluations, cost of care, lack of
providers, and lack of comfort of primary care providers to
diagnose autism. This article explores innovative clinical
models that have been implemented to address this as well
as future directions and opportunities.}
}
@Article{ pmid27957319,
author = "Fiorentino, M. and Sapone, A. and Senger, S. and Camhi, S.
S. and Kadzielski, S. M. and Buie, T. M. and Kelly, D. L.
and Cascella, N. and Fasano, A. ",
title = "{{B}lood-brain barrier and intestinal epithelial barrier
alterations in autism spectrum disorders}",
journal = "Mol Autism",
year = "2016",
volume = "7",
pages = "49",
abstract = {Autism spectrum disorders (ASD) are complex conditions
whose pathogenesis may be attributed to gene-environment
interactions. There are no definitive mechanisms explaining
how environmental triggers can lead to ASD although the
involvement of inflammation and immunity has been
suggested. Inappropriate antigen trafficking through an
impaired intestinal barrier, followed by passage of these
antigens or immune-activated complexes through a permissive
blood-brain barrier (BBB), can be part of the chain of
events leading to these disorders. Our goal was to
investigate whether an altered BBB and gut permeability is