The roles of cortisol and pro-inflammatory cytokines in assisting the diagnosis of autism spectrum disorder

Research in Autism Spectrum Disorders 9 (2015) 174–181

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The roles of cortisol and pro-inflammatory cytokines in assisting the diagnosis of autism spectrum disorder Chang-Jiang Yang a,*, He-Ping Tan a, Fu-Yi Yang a, Chun-Ling Liu a, Biao Sang a, Xiao-Mei Zhu c, Yi-Jie Du b,** a b c

School of Preschool & Special Education, East China Normal University, Shanghai, China Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, China Children’s Hospital, Fudan University, Shanghai, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 7 August 2014 Received in revised form 13 October 2014 Accepted 17 October 2014 Available online 14 November 2014

Autism spectrum disorders (ASD) is a severe neurodevelopmental disorder characterized by impairments in social interaction and repetitive behaviors. Diagnosis of ASD is currently phenotype based with no reliable laboratory test available to assist clinicians. The desire for clinically useful and reliable biomarkers is strong. Researches have shown that individuals with autism often exhibit dysfunction of hypothalamic–pituitary–adrenal (HPA) axis and cytokines. The purpose of this study was to evaluate diurnal variation of cortisol (cortisol VAR), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-a) as potential biomarkers for ASD. The present results demonstrated that in comparison to the healthy controls, the individuals with autism showed a lower level of cortisol VAR, higher level of IL-6 and TNF-a. The levels of cortisol VAR, IL-6 and TNF-a have significantly correlations with the severity of ASD measured by CARS scores. The results of ROC analysis indicated the cortisol VAR, IL-6 and TNF-a were potential biomarkers in diagnosis of ASD. The combination of three factors performed the best sensitivity and specificity for diagnosis of ASD. Therefore, the present study may reveal a simple clinical approach with great potential for assisting the diagnosis of ASD. ß 2014 Elsevier Ltd. All rights reserved.

Keywords: Autism spectrum disorders Stress Cortisol IL-6 TNF-a

1. Introduction Autism spectrum disorders (ASD) is characterized by substantial impairment in reciprocal social interaction and a markedly restricted repertoire of activities and interests in the early developmental period (American Psychiatric Association, 2013). In addition to these features, children with autism have been described as experiencing difficulty tolerating novelty and environmental stressors (Kanner, 1943). It is estimated that 1 autism case could arise in 80–240 children born, and it has been noted that the incidence showed significant increase in recent years (Yang, Tan, & Du, 2014). Despite this relatively high prevalence, our understanding of the neurodevelopmental biology and pathophysiology of this disorder remains limited. The disorder is currently diagnosed solely using core behavioral criteria selected to define ASD. However, there is presently no trusted laboratory test available to aid the clinicians.

* Corresponding author at: Department of Special Education, School of Preschool & Special Education, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China. Tel.: +86 21 62233927. ** Corresponding author at: Huashan Hospital, Fudan University, 12 Wulumuqi Road, Shanghai 200040, China. Tel.: +86 21 52888301. http://dx.doi.org/10.1016/j.rasd.2014.10.012 1750-9467/ß 2014 Elsevier Ltd. All rights reserved.

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It is shown that the individuals with autism may be easily stressed in previous studies (Bellini, 2006; Corbett, Schupp, Simon, Ryan, & Mendoza, 2010). The hypothalamic–pituitary–adrenal (HPA) axis is intimately involved in the stress response. It has been shown that dysfunction of HPA involved in the ASD (C´urin et al., 2003; Lakshmi Priya, Geetha, Suganya, & Sujatha, 2013). The HPA axis, like most biological systems, is highly regulated and dependent on the ability of the system to maintain, respond and reset itself (homeostasis). Cortisol is the primary glucocorticoid in humans. It has been well studied in many populations as it is an important measure of the biologic reactivity to stress. Both excessive and deficient cortisol responses have been associated with deregulations of the HPA axis (Gillespie, Phifer, Bradley, & Ressler, 2009). Cortisol exhibits diurnal variations with peaking in the early morning hours (about 30 min after waking), declining rapidly in the morning, with a slower decrease in the afternoon, and reaching its lowest level in the evening. This pattern is already well developed in the third month of infancy (Price, Close, & Fielding, 1983; Vermes, Dohanics, Toth, & Pongracz, 1980). Many reports suggested that children with autism showed alterations in the normal circadian patterns of cortisol (Hill, Wagner, Shedlarski, & Sears, 1977; Hoshino et al., 1987; Richdale & Prior, 1992; Tordjman et al., 1997). Therefore, the role of the relative diurnal variation of cortisol (cortisol VAR) deserves further study. Researches over the past few decades have shown immunological disturbances in ASD and a lot of studies have reported cytokines abnormalities in the peripheral blood of autistic individuals (Ashwood et al., 2011; El-Ansary & Al-Ayadhi, 2012; Goines & Ashwood, 2013; Ricci et al., 2013). In the case of developmental diseases such as ASD, the neuroimmune system could affect not only function, but also development, resulting in long-term alterations and disease (Patterson, 2002). Observations indicated significant increases in plasma level of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-a) in the individuals with autism compared with typically developing controls (Ashwood et al., 2011; Emanuele et al., 2010; Malik et al., 2011). Increase of IL-6 and TNF-a was also found in postmortem brain specimens from individuals with autism (Li et al., 2009; Wei et al., 2011). What’s more, it is reported that IL-6 and TNF-a increased in the anterior cingulated gyrus of autistic brains and also in the cerebrospinal fluid of autistic children (Vargas, Nascimbene, Krishnan, Zimmerman, & Pardo, 2005). Levels of IL-6 and TNF-a were associated with core deficits of ASD or impairments in associated behaviors (Ashwood et al., 2008; Okada et al., 2007). Recent findings suggested that proinflammatory cytokines production increased in response to psychological stress in humans (Maes et al., 1998; Steptoe, Willemsen, Owen, Flower, & Mohamed-Ali, 2001). It is known that there is an important feedback loop between cytokines and glucocorticoids: proinflammatory cytokines, such as IL-6 are potent activators of the HPA axis (Turnbull & Rivier, 1999). Glucocorticoids in turn negatively control cytokine production and by this mechanism are able to shut down inflammatory processes to prevent host destruction due to prolonged immune activity (Besedovsky & del Rey, 2000; Sapolsky, Romero, & Munck, 2000). The separate role of cortisol and proinflammatory cytokines in ASD, coupled with the evidence for interaction between them led us to examine the role of cortisol and proinflammatory cytokines as biomarkers in ASD. Therefore, the present study aimed to evaluate the levels of cortisol VAR, IL-6 and TNF-a in individuals with autism compared with typically developing controls. We also examine the connections between levels of cortisol VAR, IL-6, TNF-a in autistic individuals and severity of ASD respectively. Importantly, the present study assessed roles of cortisol VAR, IL-6 and TNF-a as potential biomarkers in assisting the diagnosis of ASD. 2. Method 2.1. Participants The participants were recruited from area schools or autism outreach groups. The study was made of thirty-eight autistic individuals and thirty-two healthy individuals in control after clinical evaluations. Participants were placed in one of two groups: (1) diagnosed with ASD or (2) confirmed as typically developing controls. For the ASD group, all of the individuals met the DSM-IV-TR diagnostic criteria for ASD (American Psychiatric Association, 2000). Participants were excluded from the study if they had a diagnosis of fragile X syndrome, epileptic seizures, obsessive–compulsive disorder, affective disorders, or any additional psychiatric. Also excluded were those with inflammation, known endocrine, cardiovascular, pulmonary, liver, kidney or neurological diseases. The control individuals were normally developing, healthy individuals, unrelated to the autistic participants and without any of the exclusion criteria. Two groups of individuals were matched on age and gender, and they were not taking any medication that could interfere with endocrine or inflammation four weeks prior to the screening and in good health at time of blood draw. The intelligence quotient (IQ) was based on the previous recording in the hospital. Three autistic individuals were drop from the study due to refuse to phlebotomize. Thus, the participants consisted of thirty-five autistic individuals and thirty-two healthy control individuals. An informed consent was obtained from the parents of each individual case prior to inclusion in the study. The ethical committee of East China Normal University approved this study. 2.2. Laboratory assessment 2.2.1. Sample collection For cortisol collection, we used saliva sampling, a noninvasive method, to avoid stressors (Kirschbaum & Hellhammer, 1994). For the ASD group, it was deemed particularly important to minimize novelty in the collection procedure, so saliva samples were collected at home on weekend. A total of eight salivary samples were collected from each research participant to obtain the cortisol diurnal rhythm. Salivary cortisol were collected immediately upon waking (around 06:00), 30 min post

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waking, 08:00, 12:00, 16:00, 20:00, just before going to sleep (around 22:00), waking on the second day. Samples were collected by using a Salivette (Sarstedt, Rommelsdorf, Germany), which contains a small cotton tube placed in the patient’s mouth to be gently chewed for 1–2 min to soak up the saliva. The cotton tube was placed back in the upper compartment of the Salivette and retrieved by technical staffs. The Salivette was centrifuged and the chamber containing the cotton tube was removed and the saliva samples were frozen at 20 8C to precipitate mucins. Then it was centrifuged at 1500  g  15 min, and the supernatant was collected and stored at 80 8C until analysis time. For cytokines collection, blood samples were used. After overnight fast, a tube of 10 mL blood sample was collected from each subject in both groups in acid–citrate dextrose Vacutainers (BD Biosciences; San Jose, CA) between 08:00 and 09:00. The tube was centrifuged at 3500  g at 4 8C for 15 min. Plasma was obtained and deep frozen (at 80 8C) until analysis time. 2.2.2. Biochemical analysis Salivary cortisol concentrations were measured by using a modification of a commercially available radioimmunoassay (RIA) kit (Acthrel, Ferring, Tarrytown, NY) as previous described (Du et al., 2014). Respectively the means of inter- and intra-assay coefficients of variation were 4.6% and 5.6%. The IL-6 and TNF-a in plasma were measured by using the quantitative sandwich enzyme immunoassays with enzyme-linked immunosorbent assay kits (R&D Systems Europe Ltd.) following manufacturer’s instructions. The detection limits for IL-6 and TNF-a were 2.0 and 3.5 pg/mL respectively. The inter-assay coefficient of variability of the cytokine assays was <10%, and the intra-assay coefficient of variability was <10% across the range of concentrations. 2.3. Behavioral assessment The Childhood Autism Rating Scale (CARS) score was completed as a measurement of the severity of autism. CARS consists of 15 domains (relating to people; emotional response; imitation; body use; object use; listening response; fear or nervousness; verbal communication; non-verbal communication; activity level; level and reliability of intellectual response; adaptation to change; visual response; taste, smell and touch response; and general impressions). Each domain is scored on a scale ranging from one to four, with higher scores associated with a higher level of impairment. An individual with a CARS score 30 is considered to have autism (Schopler, Reichler, DeVellis, & Daly, 1980). 2.4. Statistical analysis Results were performed using SPSS 17.0 statistical software (SPSS Inc., Chicago, IL). A p value of <0.05 was considered significant. Group comparisons to categorical variables were performed by using Pearson’s chi-square test or Fisher’s exact test. Continuous variables were used Student’s t-test for normally distributed data and the Wilcoxon rank-sum test for non-normally distributed data. The relative diurnal variation (VAR) was calculated as the change over 24-h period. Salivary cortisol VAR = (cortisol MAX minus cortisol MIN) divided by cortisol MAX and multiplied by 100%. To test the specificity and sensitivity of the biological markers cortisol VAR, IL-6 and TNF-a to detect autism, receiver operating characteristics (ROC) analysis was performed. The correlation between the true positive rate (sensitivity) and the false-positive rate (1-specificty) was represented as a curve. The cutoff point was chosen to minimize the sum of false-positive and false-negative test results. Efficient screening instruments are indicated by ROC curves with a high area under the curve (AUC). Spearman rank order correlations analysis was used to determine the relationships between cortisol VAR and CARS scores, IL-6 and CARS scores, TNF-a and CARS scores. 3. Result 3.1. Demographic features Demographic features between two groups were similar. There were no statistically significant differences between the groups with respect to the differentiations of age, sex, body mass index (BMI) (Table 1). Table 1 Clinical and demographic features of individuals with autism and control.

Age (mean  SD) Gender Male Female BMI IQ MiR MoR SeR

ASD (n = 35)

Control (n = 32)

p

10.63  2.64

11.19  2.61

0.387

28 7 22.32  3.35

25 7 21.67  3.78

0.850

14 16 5

0 0 0

0.461 – – –

N, number of participants; BMI, body mass index; IQ, intelligence quotient; MiR, mild mental retardation (IQ between 50 and 70); MoR, moderate mental retardation (IQ between 30 and 50); SeR, severe mental retardation (IQ < 30).

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3.2. The circadian rhythm and production of cortisol The mean patterns of salivary cortisol levels for individuals with autism and healthy control were illustrated in Fig. 1A. For autistic individuals, salivary cortisol was secreted in a flatted circadian pattern compared with healthy control, with zenith at 30 min post waking, and nadir at bedtime. To assess the presence of a disturbed circadian function of the HPA axis, the diurnal amplitude in cortisol concentration was evaluated by calculating the cortisol VAR. The individuals with autism showed a decreased median cortisol VAR of 51.00% (range, 17.36–88.04%), whereas the control individuals exhibited a median cortisol VAR of 80.74% (range, 62.31– 98.80%), p < 0.001. Indicating reduced diurnal amplitude in the cortisol concentration in ASD (Fig. 1B). 3.3. The production of cytokines There was a significant difference noted in median plasma concentration of IL-6 and TNF-a between the ASD and control individuals. The individuals with autism showed an elevated median IL-6 concentration of 224.98 pg/mL (range, 89.62– 399.26 pg/mL), whereas the control individuals exhibited a median concentration of only 98.54 pg/mL (range, 77.27– 267.59 pg/mL), p < 0.001 (Fig. 2A). The individuals with autism showed an elevated median TNF-a concentration of 216.62 pg/mL (range, 104.09–493.27 pg/mL), whereas the control individuals exhibited a median concentration of only 121.25 pg/mL (range, 66.67–243.47 pg/mL), p < 0.001 (Fig. 2B). 3.4. ROC curve analysis To assess the usefulness of these factors as adjunct in the diagnosis of ASD, an ROC analysis was performed. The optimal cut-off point for using cortisol VAR as a biomarker for ASD was 73.86%. This cut-off point was associated with a sensitivity of 91.43% and a specificity of 81.25%. (AUC = 0.93; 95% CI, 0.84–0.98, p < 0.0001). The optimal cut-off point for using IL-6 as a biomarker for ASD was 179.70 pg/mL. This cut-off point was associated with a sensitivity of 77.14% and a specificity of 93.75%. (AUC = 0.89; 95% CI, 0.79–0.95, p < 0.0001). The optimal cut-off point for using TNF-a as a biomarker for ASD was 155.28 pg/mL. This cut-off point was associated with a sensitivity of 82.86% and a specificity of 81.85%. (AUC = 0.89; 95% CI, 0.79–0.96, p < 0.0001). The combination of three factors had a sensitivity of 91.43% and a specificity of 96.87% (AUC = 0.97; 95% CI, 0.90–1.00, p < 0.0001) (Fig. 3A and Table 2). In addition, the diagnostic ability was significantly improved by the combination of three factors when compared with cortisol VAR (p = 0.035), plasma IL-6 (p = 0.025) and TNF-a (p = 0.023), however, no differences were identified between cortisol VAR, IL-6 and TNF-a. 3.5. Correlations analysis For autistic individuals, the relationship between the levels of cortisol VAR, IL-6, TNF-a and severity of autism measured by the CARS scores was also evaluated. There were negative correlation between cortisol VAR and CARS scores (R = 0.560, p < 0.001), and positive correlations between IL-6 and CARS scores (R = 0.638, p < 0.001), TNF-a and CARS scores (R = 0.699, p < 0.001) (Fig. 3B–D).

Fig. 1. Circadian rhythm of salivary cortisol concentrations (mean  SEM) (A) and median diurnal variation of cortisol (cortisol VAR) were measured and compared between autistic individuals and control individuals. C1 – immediately after awakening (around 06:00); C2 – 30 min after awakening; C3 – 08:00; C4 – 12:00; C5 – 16:00; C6 – 20:00; C7 – just before going to sleep (around 22:00); C8 – awakening the next day. ***p < 0.001 in comparison with the control individuals.

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Fig. 2. Median plasma concentration of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-a) (B) for autistic individuals and control individuals. ***p < 0.001 in comparison with the control individuals.

4. Discussion The purpose of this study was to explore the plausible use of peripheral biomarkers in assisting the diagnosis of ASD. The present results demonstrated that as compared to the healthy controls, the individuals with autism showed a lower level of cortisol VAR, higher level of IL-6 and TNF-a. The ROC analysis indicated the cortisol VAR, IL-6 and TNF-a were the potential biomarkers in diagnosis of autism. The combination of three factors performed the best sensitivity and specificity in diagnosis of ASD. What’s more, cortisol VAR, IL-6 and TNF-a were associated with the severity level of autism measured by the CARS scores. In the present research, individuals with autism exhibited decreased cortisol VAR compared with control group. This finding extended the previous researches. Cortisol is a circadian hormone and secreted by the hypothalamic–pituitary– adrenal (HPA) axis that plays a number of important functions in humans. Examination of the diurnal cycle and deviations from the cycle may provide clues as to the function of the HPA axis (Kirschbaum, Kudielka, Gaab, Schommer, & Hellhammer, 1999). The previous work in individuals with autism focused on the cortisol awakening response (CAR) and slope of the circadian in cortisol (Corbett, Mendoza, Abdullah, Wegelin, & Levine, 2006; Corbett, Schupp, Levine, & Mendoza, 2009; C´urin et al., 2003; Spratt et al., 2012; Zinke, Fries, Kliegel, Kirschbaum, & Dettenborn, 2010), however, the findings were not entirely consistent. This may be due to the changes were not big enough to be statistical differences. So in the present study, the cortisol VAR was calculated. Peak cortisol levels are observed shortly after awakening with steadily decreasing values thereafter in the absence of significant external stimulation. The trough of cortisol secretion is reached around midnight with only minimal levels of this steroid detectable (Deinzer, Kirschbaum, Gresele, & Hellhammer, 1997; Opstad, 1992). In the present study, the maximal cortisol concentrations were at 30 min post waking and minimal concentrations was at bedtime. The minimal cortisol concentrations at 0:00 may be more accurate as previous research (Du et al., 2014); however, this may disrupt the circadian rhythm of autistic individuals, which made them distressed. Recent studies have reported an association of cytokines with ASD. Chronic inflammatory diseases and immune dysregulation have been described in autistic children and adults (Rossignol & Frye, 2011). Peripheral blood mononuclear cells and lymphoblasts from individuals with ASD produced excessive proinflammatory cytokines (IL-6 and TNF-a) both basally (Malik et al., 2011) and after lipopolysaccharide (LPS) stimulation (Jyonouchi, Sun, & Le, 2001) as compared with controls. The results of this study were consistent with the findings above. The alteration of IL-6 and TNF-a were reported most in studies about ASD (Goines & Ashwood, 2013). In addition, IL-6, and TNF-a are known to be key factors in the generation of reprioritized behaviors (Depino, 2013). Researches previous indicated that endogenous IL-6 both at the periphery and in the brain participates in the development of sickness behavior, such as social exploration (Bluthe´, Michaud, Poli, & Dantzer, 2000). A biomarker can be defined as a biological variable associated with the disease of interest across and within individuals, measurable directly in a given patient or in his/her biomaterials using sensitive and reliable quantitative procedures (Gabriele, Sacco, & Persico, 2014). Although some researches regarded cortisol and cytokines (Abdallah et al., 2013; Ratajczak, 2011) as the potential biomarkers for ASD, only association analysis was used in most of studies. However, it is not sufficient to define the biomarker only by correlation analysis. In the present research, ROC analysis was performed to assess the usefulness of these biomarkers. We found that, compared to cortisol VAR, IL-6 and TNF-a independently, the combination of three factors performed the best sensitivity and specificity in diagnosis of ASD, and the AUC is 0.97. The AUC provides a useful metric to compare different biomarkers. Whereas an AUC value close to 1.00 indicates an excellent diagonal and predictive marker, a curve that lies close to the diagonal (AUC = 0.5) has no diagnostic utility. AUC close to 1.00 is always accompanied by satisfactory values of specificity and sensitivity of the biomarker. The high sensitivity means that in most

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Fig. 3. Receiver operating characteristics (ROC) curves for cortisol VAR, IL-6 and TNF-a and combined of three factors as biomarkers for ASD (A). Correlations between cortisol VAR and CARS scores (B), IL-6 and CARS scores (C) and TNF-a and CARS scores (D).

cases the ASD will be identified, while the high specificity means that few individuals whose positive test results on the ASD prediction were false. This shows their usefulness as predictive biomarkers. This could be supported by the high sensitivity and specificity recorded through ROC analysis. To our knowledge, this is the first study to use the combined ROC curve to analysis the potential biomarkers for assisting the diagnosis of ASD. The correlation analysis demonstrated that, salivary cortisol VAR showed a negative correlation with the severity of ASD, while the cytokine biomarkers showed a significant positive correlation with the severity of ASD. Indicating the cortisol and cytokines has important roles in the pathogenesis of autism. The present study has some limitations. Firstly, diagnostic procedures applied in USA/Europe usually using the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule Generic was not used in the diagnostic process in China. This shortcoming was met by long clinical experience by the psychiatric who was aware of the core behaviors in autism stated by the American Academy of Pediatrics in its Embargo from 2007 (Johnson & Myers, 2007). Secondly, this study used a small sample, because we choose to restrict participation to a small, well-characterized sample of ASD. Findings require replication in a larger group of individuals with autism. Thirdly, mechanistic studies on animal model of ASD are also

Table 2 Values of area under the ROC curve (AUC), sensitivity and specificity for the optimal cut-off point. No.

Variables

AUC

95% CI

Sensitivity (%)

Specificity (%)

Cut-off point

p-Value

A B C A, B and C

Cortisol VAR (%) IL-6 (pg/mL) TNF-a (pg/mL)

0.93 0.89 0.89 0.97

0.84–0.98 0.79–0.95 0.79–0.96 0.90–1.00

91.43 77.14 82.86 91.43

81.25 93.75 81.85 96.87

73.86 >179.70 >155.28 > 649.77

<0.0001 <0.0001 <0.0001 <0.0001

180

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important. It is possible that a closer analysis of the deep relations of cortisol, IL-6, TNF-a and ASD will offer insights for novel pharmacologic treatments. 5. Conclusions The individuals with autism showed a lower level of cortisol VAR, higher level of IL-6 and TNF-a. The results of ROC analysis indicated that the cortisol VAR, IL-6 and TNF-a were the potential biomarkers in assisting the diagnosis of ASD. The combination of three factors performed the best sensitivity and specificity in diagnosis of ASD. The present study may supply a simple clinical approach for aiding the diagnosis of ASD. Conflict of interest No potential conflict of interests relevant to this article was reported. Acknowledgments This research was supported by National Natural Science Foundation of China (No. 81401130, 31371043), Shanghai philosophy and social science planning projects (2014JJY002), Shanghai Education Program (B14006), grants from the East China Normal University (No.7119297K, 42800-401231-14006/003) and Shanghai Pujiang Program(13PJC037). References Abdallah, M. W., Larsen, N., Grove, J., Norgaard-Pedersen, B., Thorsen, P., Mortensen, E. L., et al. (2013). Amniotic fluid inflammatory cytokines: Potential markers of immunologic dysfunction in autism spectrum disorders. World Journal of Biological Psychiatry, 14, 528–538. Ashwood, P., Enstrom, A., Krakowiak, P., Hertz-Picciotto, I., Hansen, R. L., Croen, L. A., et al. (2008). Decreased transforming growth factor beta1 in autism: A potential link between immune dysregulation and impairment in clinical behavioral outcomes. Journal of Neuroimmunology, 204, 149–153. 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