Background: Autism is a multifactorial condition in which a single risk factor can unlikely provide comprehensive explanation for the disease origin. Moreover, due to the complexity of risk factors interplay, traditional statistics is often unable to explain the core of the problem due to the strong inherent nonlinearity of relationships. The aim of this study was to assess the frequency of 27 potential risk factors related to pregnancy and peri-postnatal period. Methods: The mothers of 45 autistic children and of 68 typical developing children completed a careful interview. Twentyfour siblings of 19 autistic children formed an internal control group. results: A higher prevalence of potential risk factors was observed in 22 and 15 factors in external control and internal control groups, respectively. For six of them, the difference in prevalence was statistically significant. Specialized artificial neural networks (ANNs) discriminated between autism and control subjects with 80.19% global accuracy when the dataset was preprocessed with TWIST (training with input selection and testing) system selecting 16 out of 27 variables. Logistic regression applied to 27 variables gave unsatisfactory results with global accuracy of 46%. conclusion: Pregnancy factors play an important role in autism development. ANNs are able to build up a predictive model, which could represent the basis for a diagnostic screening tool. a utism is a specific neurodevelopmental condition that typically displays qualitative socio-communicative impairment and restricted, stereotyped interests and activities (1). Although a large proportion of children with autism manifests abnormal development during the first year of life, 15-62% of them show a regression between 18 and 24 mo of age after a period of apparently typical development (2, 3) . Approximately 70% of individuals with autism present a variable degree of intellectual disability (4) and expressive/ receptive language can be absent or very insufficient (5). Other problems, not exclusive of autism, are attention-deficit and disturbed behaviors as etero-autolesivity. Thirty percent of children manifest epileptic seizures by late childhood or adolescence and 10% of cases are associated with several genetic disorders such as tuberous sclerosis, Angelman syndrome, phenylketonuria, and fragile X syndrome (6). The etiopathogenesis of autism is not yet understood; the prevalence is undoubtedly rising, and it is not clear if this increase is linked to the diagnostic improvement or to a greater susceptibility of the population to the disease. Many twins and family studies point out the importance of inherited predisposition to the disorder even though epidemiologic research suggest the strong contribution of prenatal and early postnatal environmental factors. In fact, genetic factors alone account for no more than 20-30% of all cases, whereas other 70-80% are the result of a complex interaction between environmental risk factors and inherited or de novo genetic susceptibility (7). Many pregnancies and obstetric complications have been correlated in several studies to a greater risk of autism (8-11). Recent meta-analyses (12,13) underline the role of advanced maternal and paternal age, gestational infections, low birth weight, fetal hypoxia, preterm birth, and labor complications in the pathogenesis of autism. There is still a debate in international literature about some perinatal risk factors, for example, it is not clear if there is an association between autism and neonatal hyperbilirubinemia (13, 14) . Many studies have investigated the role of prenatal maternal smoking (15), parental psychiatric conditions (16,17) including baby blues symptoms (8, 15) , and depressive/anxious traits of parents and siblings (18). Less prominence has been given to maternal stress and stressful life events during pregnancy or in first months after birth (15, 19) . Psychological stress during pregnancy increases the secretion of corticotropin-releasing hormone from the hypothalamus and regulates the hypothalamic-pituitaryadrenal axis. Elevated plasma levels of corticotropin-releasing hormone correlate to preterm labor and to anxiety perceived during pregnancy. Acute stress also elevates serum levels of interleukin-6 produced by mast cells, responsible for neuroinflammatory response and disruption of gut-blood barrier and blood-brain barrier permitting neurotoxic molecules to enter the brain, contributing to autism pathogenesis and symptoms (8). After delivery, maternal stress, in particular during the first 6 mo of life of Autism is a multifactorial disease in which a single risk factor unlikely can provide comprehensive information. Moreover, due to the complexity of interplay of risk factors, traditional statistics is often unable to grasp the core of the problem due to the strong inherent nonlinearity of relationships. Artificial neural networks (ANNs) are artificial adaptive systems, inspired by the functioning processes of the human brain. These computational systems are able to modify their internal structure in relation to a function objective. So, they are particularly suited for solving nonlinear problems, being able to reconstruct the fuzzy logic rules that govern the optimal solution for these problems. The ability to learn through an adaptive way (i.e., extracting from the available data the information needed to gather a specific task and to generalize  Articles the acquired knowledge) is a characteristic that makes the ANNs models a very powerful tool for data analysis even in presence of relatively small samples. The internal structure and functional organization of such systems can be updated and modified with respect to the environmental changes enabling the ANN to create its own representation of the information. Moreover, these models are interesting also for their noise tolerance that allows accurate performances in the presence of unreliability, wrong data, or measurement errors. In recent years, ANNs have been used successfully in medicine. For example they have been used to investigate the predictive values of risk factors on the conversion of amnestic mild cognitive impairment to Alzheimer's disease (23), to identify placental determinants of fetal growth (24), to identify genetic variants essential to differentiate sporadic amyotrophic lateral sclerosis cases from controls, and to distinguish between Alzheimer's disease patients and controls, among others. Although ANNs offer promise for improving the predictive value of traditional statistical data analysis and have been successfully used in many areas of medicine, no reports have so far investigated the ability of ANNs in predicting autism risk. The aims of this pilot study were to evaluate the differential frequency of several potential risk factors related to pregnancy, peri and early postnatal period investigated through a careful interview of mothers of autistic children in comparison with mothers of typicals, and to assess the predictive capacity of ANNs in distinguishing consistently these two different conditions employing rigorous validation protocols. The mean Hollingshead index of parents of autistic children resulted significantly lower in comparison with that recorded in control group parents. T-test gave a P value < 0.0001. Table 1 shows the comparison of frequencies of different risk factors in the autistic and external control groups. A statistically significant difference was found for the following six factors, which were all more frequent in autistic group: solvents/paints occupational exposure during pregnancy (P = 0.004), stressful events during pregnancy (P = 0.012), pregnancy complications (P = 0.008), perinatal complications (P = 0.047), deficient breastfeeding after delivery (P = 0.037), and early antibiotic therapy of the newborn (P=.047). The results obtained with multivariable logistic regression were rather unsatisfactory, with very poor sensitivity rate (0.33) and specificity rate (0.59); overall accuracy = 0.46. The application of TWIST algorithm to eliminate noisy variables before the main test of pattern recognition allowed the selection of 16 attributes (given in bold in Table 1 ). It is interesting to note that this evolutionary hybrid algorithm adaptively selected variables even with very low correlation index to build up the best predictive model to discriminate between cases and controls, confirming the well-known predisposition of ANNs to handle nonlinearity without major problems. Table 2 shows the predictive results obtained with back-propagation ANN and Figure 1 the relative receiver-operating characteristic (ROC) curve. ANNs reach an average global accuracy rate of 80.19%. We also had the opportunity to recall the pregnancy history from 19 mothers with autistic children related to 25 siblings. In this way, a much matched case-control comparison was possible. Table 3 shows the frequencies of different factors in these two groups. Beyond statistical significance, which was not reached in any of the factors due to the small sample size, it is interesting to note how most of the factors in absolute terms were more frequent in autistic group, in particular, solvents/ paint exposure, stressful events, perinatal complications, low gestational age, deficient breastfeeding, and early antibiotic therapy, mirroring the situation of the comparison with typical group. Due to small sample size, ANNs application was not feasible; therefore, for this subgroup, nonmultivariate analysis has been performed. This study represents a new model for epidemiological survey in the autism field and the first attempt to our knowledge to develop a predictive model for autism risk derived by factors collected during and immediately after the pregnancy Articles Grossi et al. with ANNs. In our opinion, some points of strength reserve consideration. The first aspect is related to the selection of potential risk factors from an accurate review of published epidemiological case-control studies published in the literature. The selection has taken into account many factors beyond both their actual relevance in the published report from statistical point of view, considering their plausibility in relation to the most recent pathogenic theories. In fact, statistical relevance in epidemiology is often obscured by nonlinear functions, which hamper the execution of classical statistical tests. On the other hand, since we employed ANNs, we were aware of the possibility to build up efficient models also with variables having poor linear relation with the target variable, represented by diagnostic class. ANNs in fact were able to build up a predictive model with high sensitivity and specificity with a nonparsimonious selection of variables despite the relatively small sample size. ANNs are computerized systems that use nonlinear statistical analysis to reveal previously unrecognized and/or weak relationships between the input and output variables. These multilayered connectionist models are adaptive; that is, they dynamically find the connections between various datasets. ANNs also have the capability of generalization: when an ANN has been trained with proper data for determining the rules that better describe a certain phenomenon, it also can make some correct generalizations responding correctly to data it has not yet processed. In other words, after a learning period, the ANN can predict the output on inputs of further unknown cases.  Pregnancy risk factors in autism ANNs have proved to be superior to classical logistic regression in cases in which excessive collinearity among independent variables or poor linear correlation indexes among independent and dependent variables impair the logistic modeling. The second aspect is the use of a structured direct interview of the mothers performed by a medical doctor, always the same. Most studies published in this field have used data derived from computerized health records stored in large hospital databases. It is clear that these databases in case of retrospective studies cannot address efficiently delicate factors as life stressful events or behavioral lifestyles. The confidence posed by mothers in the professional skill of educators and therapists of the center and in particular to the medical doctor interviewing was so strong that none of the mothers refused the informed consent to cooperate in the one-to-one long interview. This direct interview with confident attitude can explain at last in part the unusually high frequency of some specific factors encountered in our study. We observed a similarity in differential pattern of frequencies both in external case-control and in internal case-control comparison with a strong contribution of solvents/paint exposure, stressful events, perinatal complications, low gestational age, no breastfeeding, and early antibiotic therapy. This in our view is a remarkable sign pointing out the reliability of the findings, especially when the same mother is control of herself. The parental occupational exposure has already been object of study (25) and a link between some chemicals and the presence of autism spectrum disorders in the offspring seems to be more and more realistic. In our interviews, we did not start from a specific list of chemical agents, but we first asked the mothers to describe what kind of job or task they had when they were pregnant and then we retraced the toxic agents involved. It is important to note that some women, who worked in factories in close contact to potentially toxic agents, left the place of work only after discovering the pregnancy, so the fetus during the first weeks of gestations was not protected. The increased prevalence of breastfeeding deficiency after delivery, early antibiotic therapy of the newborn, and cesarean section in autistic group, both in external and internal control comparison, deserves a special comment, since all these three factors are able to impair the gut microbiota of the newborn. There is a unanimous consensus about the fact that vaginal delivery and breastfeeding are essential in providing the appropriate starting bacterial substrate for the development of a physiological gut flora. Early antibiotic therapy obviously can influence the equilibrium of intestinal flora as well. The possibility that autism is the consequence of an imperfect development of gut flora is supported by a number of observations like: the frequent coexistence of gastrointestinal symptoms in autistic children, the appearance of the disease after an incidental antimicrobic therapy, and the increased levels of urinary biomarkers of specific pathogens of Clostridium spp. in the urine of autistic children (26). In a recent study, the authors demonstrated that in a particular model of autistic mouse displaying behavioral symptoms relevant to autism spectrum disorders and other neurodevelopmental disorders, dysbiosis of the commensal microbiota, and alterations in serum metabolites are treated by the administration of a particular commensal (Bacteroides fragilis). These findings represent a major breakthrough in the microbiota hypothesis of autism spectrum disorder (27) . The role of stressful events in contributing to increased autism risk also deserves a special attention since very few studies have attempted to collect this kind of information. Adverse experiences during the prenatal period (a time of rapid growth and of heightened brain plasticity) have been demonstrated to induce significant effects on neurobiology, metabolism, and physiology that can persist across the life span (28). Generally, the more variable the stressor and the earlier the stressors occur in pregnancy, the more profound the effect on offspring development. Neurobiological studies on animals show, on male offspring, that stressful events in pregnancy affects prefrontal and temporal cortical regions. Studies in rats showed that the development of layer II/III pyramidal neurons in the dorsal anterior cingulate and orbitofrontal cortex is significantly affected in offspring of mothers exposed to stress during pregnancy. Moreover, in both cortical areas, a significant reduction of dendritic length was observed in males (29). Previous Articles Grossi et al. study (30) shows a significant effect of emotional stress experienced by rats before pregnancy on brain development of their offspring. In 21-d-old rats, layer V neuronal nuclei were enlarged, while the thickness of the parietal cortex tended to decrease. Thirty-day-old experimental rats demonstrated low exploratory activity in the plus-maze test. Moreover, offspring rats of mothers exposed to stress during pregnancy show that interleukin-1β exposure had reduced estradiol content in hippocampus, medial prefrontal cortex, and diencephalon (31). The precise neurobiological mechanisms of action underlying these phenomena remain unknown. Recently, in a small, explorative study, a focal disruption of cortical laminar architecture was found in prefrontal and temporal cortical tissue of a majority of young children with autism. These data support a probable dysregulation of layer formation and layer-specific neuronal differentiation at prenatal developmental stages (32). The third remarkable aspect is the use of ANNs to handle the data and to overcome the sample size limits. ANNs aim to understand natural processes and recreate those processes using automated models. These networks determine a noteworthy improvement, compared with traditional methods of analysis, and allow a method of forecasting with better and safer understanding of the relationship between variables, in particular, nonlinear relationships. ANNs function by initially learning a known set of data from a given problem with a known solution (training), and then the networks, inspired by the analytical processes of the human brain, are able to reconstruct the imprecise rules which may be underlying a complex set of data (testing). Using these tools has been possible to achieve a consistent predictive rate of pregnancy outcomes across different experiments with a ROC area under the curve (AUC) equal to 0.795. This value, following Sweet and Picket criteria, can be considered as moderate diagnostic accuracy. The interpretation of the AUC values is traditionally as follows: an AUC < 0.7 suggests "low" diagnostic accuracy; an AUC from 0.7 to 0.9 suggests "moderate" diagnostic accuracy, and an AUC ≥ 0.9 suggests "high" diagnostic accuracy (33). There are also some substantial weaknesses in the present study, key among them the understandably small sample size which makes difficult to be fully confident in the statistical estimates. In particular, the small sample size creates situations in which high odds ratio values do not convey necessarily significant P values (this occurs for example when the odds ratio of postpartum depression was calculated in Table 1 or pregnancy complications in Table 3 ). Another limitation is the imperfect matching of cases and external controls in terms of geographical distribution. Nevertheless, mothers could and did complete the survey, even if the recall sometimes was hard, and this is meaningful for them. This implies that the protocol of this pilot study is feasible and can easily be replicated in larger sample. An additional limitation could be the retrospective self-report of occupational exposure to toxic agents, which may result in bias and misclassification. Anyway this potential memory-based bias is equal in the three groups (clinical sample, internal control, and external control). The authors feel confident to draw the following conclusions: 1. the frequency of some risk factors like exposure to solvents or paints, stressful events, pregnancy complications, perinatal complications, among others, is significantly higher in autistic group compared with external and internal control children; 2. ANNs are able to build up a predictive model with high sensitivity and specificity with a nonparsimonious selection of variables despite the relatively small sample size; 3. the use of ANNs may be an important advance research on complex disorder as autism. Case features. Forty-five children and adolescents received independent diagnoses of autism according to DSM-5 criteria, then confirmed by a qualified child and adolescent psychiatrist at Villa Santa Maria, Tavernerio (Italy) where the patients reside. The 45 cases selected are those children whose mothers gave their informed consent. Among the pregnancies of the patients selected for the study, 37 occurred in Lombardy (a northern region of Italy), 5 in central and southern Italian regions, and 3 in European countries (e.g., Romania, Albania, Spain). All the participants showed a comorbid intellectual disability. All subjects were assessed with the Childhood Autism Rating Scale, and the scores had a range from a minimum of 30 to a maximum of 60 points; in particular, 8 subjects were assessed as affected by mildto-moderate autism and 37 by severe autism. No autistic child was affected by genetic conditions, cerebral malformations documented by neuroimaging, and epilepsy. The socioeconomic status was calculated with the Hollingshead index (AA Hollingshead, personal communication), a measure of social status of an individual based on educational attainment and occupational prestige. The child participant's parent's education code is rated on a 7-point scale that lists highest grade completed, in which 7 = graduate/professional training, 6 = standard college or university graduation, 5 = partial college, at least 1 y of specialized training, 4 = high school graduate, 3 = partial high school, 10th or 11th grade, 2 = junior high school, including 9th grade, 1 = less than 7th grade, 0 = not applicable or unknown. The child participant's parent's occupational code is rated on a 9-point scale, for which the Hollingshead manuscript provides a more detailed list: 9 = higher executive, proprietor of large businesses, major professional, 8 = administrators, lesser professionals, proprietor of mediumsized business, 7 = smaller business owners, farm owners, managers, minor professionals, 6 = technicians, semi-professionals, small business owners (business valued at $50,000-70,000), 5 = clerical and sales workers, small farm and business owners (business valued at $25,000-50,000), 4 = smaller business owners (<$25,000), skilled manual laborers, craftsmen, tenant farmers, 3 = machine operators and semi-skilled workers, 2 = unskilled workers, 1 = farm laborers, menial service workers, students, housewives, (dependent on welfare, no regular occupation), 0 = not applicable or unknown. socioeconomic status of the clinical sample was equal to 2.55 (SD 1.07). The recruitment of controls started with a public announcement spread in the geographical area of Tavernerio, where Villa Santa Maria is set. Sixty-eight mothers of typical developing children answered and decided to participate (the participation rate was 100%). In particular, 31 subjects were children of the staff members of Villa Santa Maria and 37 were children of women living in the area. The control subjects recruited were 29 females and 39 males, with a mean age of 9.13 y (SD 4.20). All the pregnancies occurred in Lombardy region. None of these subjects claimed symptoms related to autism spectrum disorders or learning disabilities. The mean socioeconomic status of the control group was equal to 3.51 (SD 1.01).
Grossi et al. reported the use of specialized artificial neural networks (ANNs) to build up a predictive model based on 27 potential pregnancy risk factors in autism development REF .
Pregnancy risk factors in autism: a pilot study with artificial neural networks
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