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Background:
Hypothesis

The Possible Role of Postnatal Biphasic Dysregulation of IGF-1 Tone in the Etiology of Idiopathic Autism Spectrum Disorder

by
András Visegrády
Pharmacology and Drug Safety Research, Gedeon Richter Plc., Gyömrői út 19-21, H-1103 Budapest, Hungary
Int. J. Mol. Sci. 2025, 26(10), 4483; https://doi.org/10.3390/ijms26104483
Submission received: 4 April 2025 / Revised: 30 April 2025 / Accepted: 7 May 2025 / Published: 8 May 2025

Abstract

Autism spectrum disorder (ASD) is a pervasive condition of neurodevelopmental origin with an increasing burden on society. Idiopathic ASD is notorious for its heterogeneous behavioral manifestations, and despite substantial efforts, its etiopathology is still unclear. An increasing amount of data points to the causative role of critical developmental alterations in the first year of life, although the contribution of fetal, environmental, and genetic factors cannot be clearly distinguished. This review attempts to propose a narrative starting from neuropathological findings in ASD, involving insulin-like growth factor 1 (IGF-1) as a key modulator and demonstrates how the most consistent gestational risk factors of ASD–maternal insulin resistance and fetal growth insufficiency–converge at the perinatal dysregulation of offspring anabolism in the critical period of early development. A unifying hypothesis is derived, stating that the co-occurrence of these gestational conditions leads to postnatal biphasic dysregulation of IGF-1 tone in the offspring, leading first to insulin-dependent accelerated development, then to subsequent arrest of growth and brain maturation in ASD as an etiologic process. This hypothesis is tested for its explanation of various widely reported risk factors and observations of idiopathic ASD, including early postnatal growth abnormalities, the pervasive spectrum of symptoms, familial predisposition, and male susceptibility. Finally, further directions of research are outlined.

Graphical Abstract

1. Introduction

1.1. Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and the presence of restricted interests and repetitive behaviors [1,2,3]. ASD displays increasing prevalence trends [4,5] and poses a steadily increasing burden on society [6,7]. ASD is diagnosed based on behavioral symptoms, but its manifestations are notoriously heterogeneous [2,8]. Despite the high prevalence and increasing awareness, little is known about the etiology of ASD [9,10]. It is characterized by a clearly demonstrated familial risk and high heritability [11,12], and numerous genetically defined syndromes display high comorbidity with autism. Large-scale genetic studies searched for genetic background in ASD, and a high number of candidate genes have been identified with high confidence, including among others CHD8, TSC2, NRXN1, SHANK3, SHANK2, and MTHFR [13,14,15]. Despite these advancements in genomic research, idiopathic ASD still represents the majority of cases [14,16]. The disorder is typically diagnosed in toddlers or in early childhood [17], although first manifestations seem to occur around 6–12 months of age [18,19]. The diagnosis of autism is rather stable throughout childhood [20,21] and adolescence [22,23], suggesting a critical role for early brain maldevelopment in the emergence of autism symptoms [24,25]. Therefore, in addition to the heterogeneity in symptoms, studies of ASD are also hampered by the timing of symptom onset, namely incidence during a period of intense brain development and maturation of the nervous system [26,27], requiring longitudinal observations and analysis of at-risk populations placed in a developmental framework [24,28].
Recognizing the proposed early origin of the disorder, environmental, familial, and gestational risk factors have been investigated extensively, and several risk factors of idiopathic ASD have been identified [1,29]. Among familial and environmental factors, an affected sibling [11,30] or male sex [31,32] are widely documented to substantially increase the risk for autism. Furthermore, increased maternal and paternal age have also been demonstrated as risk factors [33,34]. Among gestational and maternal conditions, the most robust and consistently identified risk factors for ASD are conditions related to maternal hypertension, namely preeclampsia and gestational or chronic hypertension [29,35,36]; maternal use of antidepressants, in particular, selective serotonin reuptake inhibitors (SSRIs) [29,37,38] and symptoms related to maternal metabolic disturbances like gestational diabetes [39,40,41,42] and pre-gestational overweight or obesity [29,43,44]. For perinatal factors, signs of fetal growth insufficiency, like preterm birth [40,45] or low or very low birth weight [40,46], are associated with ASD risk, but, interestingly, macrosomia (fetal overgrowth) also bears a slight risk for later ASD diagnosis [46].
Although ASD is typically not diagnosed before the age of 18 months, at the population level, several growth and developmental deviations have been described to precede or correlate with later ASD diagnosis. Accelerated early growth has been demonstrated for body length [47,48], total brain volume [25,49,50,51], cortical surface area [52], and excess extra-axial fluid volume [53]. Importantly, following a period of overgrowth in toddlers, a slowing or even arrested growth has been reported for many of these parameters in early childhood [51,52,54,55].
In addition to that observed in anthropometric measures, a biphasic developmental trajectory was also found for brain structural connectivity [56]. In particular, two highly valuable longitudinal imaging studies, comparing at-risk infants later diagnosed with ASD to non-affected peers [57] or diagnosed toddlers to typical controls [58], reported structural hyperconnectivity between brain regions at earlier scans, in contrast to hypoconnectivity at the later time points (24- ca. 36 months, depending on the study). In studies of head circumference, direct comparisons found early overgrowth in ASD (and in pervasive developmental disorder not otherwise specified, in earlier studies), which was absent in patients with attention deficit hyperactivity disorder (ADHD) [59,60] or generalized or other developmental delays [48,61].
Despite robust data on well-defined prenatal risk factors and postnatal developmental alterations with substantial comorbidity with ASD, to date, no theoretical framework exists for the etiology of this disruptive disorder that convincingly matches these observations. In recognition of the pervasive heterogeneity of the disorder, recently, multiple etiologies have been considered to account for the diverse manifestations of the dysfunctions [62]. However, it has yet to be demonstrated that symptoms group according to suggested pathomechanisms or risk factors [8]. Moreover, multiple disjunct etiologies or genetic causation have to account for the increasing prevalence of ASD, with the disorder retaining its heterogeneity of manifestations, stable pattern of risk factors, and characteristic developmental trajectory. Alternatively, instead of multiple etiologies or risk genes, a central pathomechanism could be considered in idiopathic ASD, a process that could inherently account for the heterogeneity of symptoms and be associated with known ASD risk factors. In the latter sections, such a hypothesis of idiopathic ASD etiology is proposed and discussed in light of scientific observations on ASD.

1.2. Neuropathological Findings in ASD and a Neurodevelopmental Narrative

Despite occasional and specific peripheral comorbidities of ASD, it is reasonable to assume that the brain is the most affected organ in the disorder. Neuropathology studies of autism patients report wide-ranging microscopic brain alterations [63,64,65,66] like decreased MECP2 expression [67] and widespread disorganization of cytoarchitecture and neural migration [68,69]. The most consistent microscopic observation, demonstrated in about 75% of cases, is the decreased number of Purkinje cells (PCs) in the cerebellum [63,65]–typically in the form of dispersed and partial loss [70,71]. The origin of this cell loss was believed to be prenatal, suggested by the lack of retrograde cell loss in the inferior olive [72], however, the presence of excess cerebellar basket and stellar cells [73] and the lack of hypoplastic folia [65] suggests a relatively late timing of this event, and therefore the precise time of atrophy could not be convincingly established yet (discussed in [65]). Late postnatal loss of PCs in autism has not been considered in the literature, due to the putative dependence of inferior olivary neuron survival on their PC targets [72]. However, two findings support the possibility of postnatal atrophy. First, the dependence of inferior olive neurons on their target PCs seems to weaken during postnatal development [74,75]. Namely, although retrograde inferior olivary numeric atrophy has been demonstrated in various mutant mouse strains with early PC loss, in strains with the latest postnatal onset of PC death (around postanal days 30 and 20 in leaner and nervous mice, respectively), little or no loss of inferior olivary neurons is reported [75,76]. Secondly, although retrograde atrophy of the inferior olive has been observed in human patients following acute cerebellar lesions [77], gradual and partial loss of PCs, in contrast, does not necessarily lead to detectable inferior olivary atrophy in adults [77,78]. Intriguingly, neuropathologic analysis of the youngest ASD specimens (3 to 4 years) revealed no PC loss [63,70,79], in line with the possibility of late postnatal atrophy. In summary, PC loss in ASD in later neonatal development cannot be excluded as a possibility and might even be in line with pathological findings [73]. If PCs were present throughout the regular development of the cerebellum, their loss might be associated with later changes in the internal environment, like the sudden loss of neurotrophic or survival factors, as PCs are known for their particular vulnerability to pathophysiological insults [80,81,82].
When searching for survival-promoting agents of PCs, insulin-like growth factor 1 (IGF-1) has been identified as a hormone having a pronounced and reproducible positive effect on PC survival [83,84] and development [85] both in vitro and in vivo [86,87]. The marked survival-promoting effect of IGF-1 on PCs seems specific in comparison to numerous other neurotrophic factors like nerve growth factor, brain-derived neurotrophic factor [88,89], ciliary neurotrophic factor [90], basic fibroblast growth factor, insulin, and insulin-like growth factor 2 [83], or to contradictory effects of neurotrophin-3 [88,89] or glial cell line-derived neurotrophic factor [86,91]. Thus, although other survival-promoting factors might also play a role, PC loss in ASD might be a result of late postnatal attenuation in the neurotrophic tone of IGF-1. Strikingly, in two highly valuable clinical datasets, liquor IGF-1 level of younger ASD patients, ages 1.9 years to 5 years, was found to be extremely low and lower than that of age-matched nonaffected patients [92] or neurotypical controls [93], a difference not found for nerve growth factor [94], insulin-like growth factor 2 or IGF-1 in ASD patients above the age of 5 [93].
Previously, loss of IGF-1 tone as an underlying mechanism in ASD has been suggested [95,96,97], and IGF-1 supplementation has been proposed for the treatment of ASD or related disorders [97,98,99].

1.3. Clinical Studies of IGF-1 and Related Factors in Neurodevelopmental Disorders

Multiple clinical trials have been performed investigating the effect of IGF-1 or related factors in neurodevelopmental conditions associated with ASD. Children of 5–9 years with Phelan-McDermid syndrome, all with SHANK3 irregularities, were treated with recombinant human IGF-1 for 12 weeks in two consecutive studies [100]. Although treatment did not improve with statistical significance the primary endpoint, Aberrant Behavior Checklist—Social Withdrawal subscale in combined analysis, improvement was found in the Restricted Behavior subscale of Repetitive Behavior Scale—Revised scale and in sensory reactivity symptoms. Investigation of mecasermin (recombinant human insulin-like growth factor 1) in a double-blind study in Rett Syndrome in girls between 2 and 10 years for 20 weeks resulted in worsening of some of the primary endpoints with some improvement in measures of stereotypic behavior and social communication [101]. A pilot study with mecasermin in children with ASD (age 5–12 years) was terminated prior to completion.
In addition to IGF-1, analogues of the N-terminal tripeptide fragment (1–3) IGF-1, not a ligand of IGF-1R (IGF-1 receptor) but a factor influencing IGF-1 action, also entered clinical development. Trofinetide, a tripeptide analogue, was developed for Rett Syndrome with a significant improvement on the Rett Syndrome Behaviour Questionnaire [102] and was approved as a first-in-class drug for this indication in 2023. Additionally, it also showed a positive effect on Fragile X syndrome [103]. NNZ-2591, a synthetic analog of cyclic glycine-proline, the metabolite of (1–3) IGF-1, is currently in late-stage clinical development in multiple neurodevelopmental disorders.
The results of the above studies support a possible role of neurotrophic factors in the therapy of neurodevelopmental disorders, however, they also revealed several limitations. The timing and duration of the therapy in light of the potentially mitogenic effects of IGF-1 could be a concern despite lowered IGF-1 levels in patients [98]. Furthermore, the placebo effect in caregiver questionnaires might mask slight improvements in behavioral observations. Still, with efficacy in several genetic disorders using trophic factors, clinical development of further therapies might be accelerated.

1.4. IGF-1 and Its Dysregulation in Perinatal Complications

The hypothesis of IGF-1 deficit as the cause of ASD, unfortunately, does not account for the most consistent characteristics and risk factors of idiopathic ASD, such as early growth and connectivity anomalies, or various perinatal or familial risk factors. Furthermore, although IGF-1 regulates PC survival and development, IGF-1 knockout mice are not characterized by PC loss [104], which suggests a more complex dysregulation of IGF-1 in ASD.
IGF-1 is a trophic factor, a mediator of growth via its major molecular target, IGF-1R, ubiquitously expressed in most tissues, with the highest expression levels in neuronal cell types (Figure 1). Circulating IGF-1 is predominantly secreted from the liver but can be taken up by the brain, enabling modulation of central levels by circulating IGF-1 of liver origin [105,106]. IGF-1 is widely expressed also in other tissues [107] including the brain [84] and while paracrine action contributes to postnatal growth [108], liver-produced systemic IGF-1 also exerts substantial somatic growth-promoting effects [109,110], indicating that multiple sources of IGF-1 act jointly on tissue development and that endocrine IGF-1 also affects growth. Secretion of IGF-1 from the liver is predominantly regulated via the growth hormone-releasing hormone (GHRH)–growth hormone (GH)–IGF-1 axis [111], but peculiarly, perinatal regulation of endocrine IGF-1 is different, creating a vulnerability through a specific constellation of perinatal factors associated with later ASD risk. Finally, unlike the closely related insulin, IGF-1 is bound to a family of carrier proteins (insulin-like growth factor-binding proteins, IGFBP), which can regulate IGF-1 availability and thus exert further control on IGF-1 function in vivo.
In the fetus, IGF-1 release from the liver is regulated by circulating insulin with little effect on GH [113,114,115], also mirrored in the close-to-normal birth weight of GH-deficient infants [116]. In newborns, circulating IGF-1 is a determinant of somatic growth [117,118,119] and is associated with insulin level [120,121] with no or negative correlation to GH or GH binding protein [122,123], in line with typically no signs of growth deficit in congenital GH deficiency prior to 6–12 months [116]. Regulation of circulating IGF-1 in infants thus gradually changes from insulin-dependent to GH-dependent throughout the first year of life [123,124].
As IGF-1 is a major regulator of fetal growth, two groups of gestational maternal complications associated with metabolic and growth alterations in the offspring can lead to its dysregulation. Fetal growth restriction or intrauterine growth restriction (IUGR) often leads to a neonate with a birth weight lower than the 10th percentile. These small for gestational age (SGA) infants are characterized by short stature, decreased serum or cord IGF-1 level [125,126], and elevated insulin sensitivity at birth [126,127]. Their majority undergo a period of compensatory accelerated early postnatal growth (catch-up growth) [128,129] and reach a more insulin-resistant metabolic state [127,130,131]. An opposite alteration from the typical perinatal growth trajectory and metabolism is observed after macrosomia or fetal overgrowth. Macrosomic, or large for gestational age, newborns display hyperinsulinemia [132,133] and higher IGF-1 levels [134,135] in a more insulin-resistant state [133,136,137] and often suffer decelerated early postnatal growth–catch-down growth [138,139,140]. Catch-up and catch-down growth take place mainly within the first 6–9 months [138,139], the time window of insulin-dependent IGF-1 secretion in the newborn. SGA infants experiencing catch-up growth have elevated circulating IGF-1 levels compared to those remaining short or light [141,142].
These specific perinatal growth anomalies are highlighted because their maternal risk factors show a striking overlap with those of idiopathic ASD discussed in Section 1.1. In one group of conditions, SGA offspring and low birth weight are associated with maternal SSRI and antidepressant use [143,144], and IUGR is highly comorbid with preeclampsia [145,146] and gestational hypertension [147,148]. Conversely, in a second group of gestational conditions, macrosomia is strongly associated with maternal metabolic disturbances like gestational diabetes [149], obesity [150], or higher than recommended gestational weight gain [151], driven by insulin resistance [152]. Importantly, as discussed above, both groups of maternal conditions are strongly connected with ASD risk in the offspring [29,36,42,44].

2. Hypothesis

2.1. A Possible Etiological Role of IGF-1 Dysregulation in Idiopathic ASD

The gestational risk factors of ASD described above thus form two groups associated with opposing dysregulation of fetal growth, neonatal insulin sensitivity, circulating neonatal or cord IGF-1 level, and infant growth trajectory. More importantly, the timing of their postnatal consequences also overlaps with the earliest developmental alterations preceding ASD in infancy. Based on these overlaps, the novel hypothesis for idiopathic ASD presented here is that the simultaneous occurrence of these counteracting conditions during gestation could lead to a complex dysregulation of perinatal insulin homeostasis and insulin-mediated IGF-1 action in the affected offspring. Prenatally, these co-occurring factors might partially neutralize each other despite misalignment of fetal insulin sensitivity; however, after birth, isolated from maternal circulation, asynchronous compensatory processes could lead to biphasic dysregulation of neonatal insulin sensitivity and IGF-1 tone. In the first phase, separated from the placental unit, prevailing elevated insulin action in tissues could result in higher IGF-1 secretion, leading to accelerated growth in a sensitive period of brain development in infancy. Next, by the age of 12–15 months, maturation of the GHRH-GH-IGF-1 axis and sharp negative feedback due to elevated IGF-1 levels could lead to an abrupt drop in GHRH-GH-IGF-1 tone, and concomitantly to arrest of IGF-1-dependent developmental processes and slow maturation of the central nervous system (Table 1).
According to this hypothesis, thus, prenatal gestational anabolic disturbances result in a complexly dysregulated neonatal insulin-IGF-1 axis, leading first to elevated and later to decreased IGF-1 levels in affected infants, leading to somatic and brain overgrowth and subsequent growth arrest and delayed maturation (summarized in Figure 2). This biphasic postnatal growth differs from that of catch-up growth in SGA or low birth weight infants in that the latter is characterized by in utero growth restriction or insufficiency leading to delayed growth up to birth, while co-occurrence of maternal insulin resistance would partly compensate IUGR, resulting in a generally well-developed newborn with high capacity for overdevelopment in a postnatal compensatory growth spurt.
Since somatic growth anomalies, observed only in a subset of ASD patients, supposedly do not form the etiologic factors of ASD, core symptoms should depend on developmental alterations in affected brain regions. Brain development and connectivity of ASD brains seem to show peculiar dynamics at an early age [24,25,56]. Early connectivity abnormalities–mixed functional over- and underconnectivity–at 6 months were used to predict later ASD diagnosis with 100% specificity [153], and the alterations seem specific to ASD [154]. Additionally, brain overgrowth predominantly in cortical surface area by 6 and 12 months identified affected infants with high specificity (95%) [25]. As these developmental trajectories are distinct from those in global developmental delay [155] and ADHD [156] and as diagnosis stability of ASD is relatively high, it can be reasoned that this early period of brain overgrowth and probably overconnectivity is specific to autism and might form the pathomechanistic basis of idiopathic ASD [157]. The effect of overgrowth and subsequent arrest could be affected by genetic and environmental factors, and these, in concert, can contribute to the phenotypic heterogeneity of idiopathic ASD.
IGF-1R and its downstream PI3K/Akt/mTOR pathway have been shown to interact with processes associated with ASD and risk genes TSC2 [158], GIGYF1 [159], or CNTNAP2 [160]. IGF-1 has been shown to elicit developmental effects in models of genes with strong ASD association, among others, SHANK3 [161] and DYRK1A [162].
As described above, the potential role of IGF-1 in ASD has been recognized in the literature, and IGF-1 supplementation has been considered as a treatment for ASD [95,96,97,163,164]. The hypothesis of early postnatal elevated central IGF-1 tone or its biphasic dysregulation, however, has not been raised before. The above hypothesis thus represents a novel viewpoint of the role of IGF-1 in the appearance of idiopathic ASD.

2.2. Explanation of Observations and Risk Factors of ASD

IGF-1 is a pleiotropic modulator of growth [107] and brain development [165]; therefore, dysregulation of IGF-1 tone affects a broad range of neurodevelopmental processes and therefore could account for the most pervasive observation in ASD: the puzzling diversity of brain developmental, pathophysiological, and pathological findings, as well as the resulting variety of behavioral or neurological symptoms. According to this view, it is not a single site, but most of the organism and the central nervous system are affected by abnormal biphasic early overgrowth, and genetic and other factors could further shape the appearance of ASD. Since the highly arbored PCs of the cerebellum are known to display elevated vulnerability to pathophysiological conditions in vivo [80,82,166], their survival and development could be the most affected by altered central IGF-1 availability. Unprogrammed postnatal elevation of IGF-1 tone followed by an abrupt drop could lead to late and selective postnatal PC loss, without substantial atrophy of cerebellar basket cells, cerebellar stellar cells, and inferior olivary neurons. As cited above, the lack of PC loss in the youngest ASD cases subjected to neuropathological analysis is noteworthy, not contradicting the supposed PC loss throughout early childhood. This assumption is in line with the extremely low liquor levels of IGF-1 between 1.9 and 5 years in ASD patients [92,93]. Similarly, although peripheral and central IGF-1 do not necessarily correlate, lower serum levels of IGF-1 were found in ASD patients of 2–3 years of age compared to age-matched controls [167], and lower urinary secretion of IGF-1 has been reported in ASD in a very similar age range (2–5 years) [168]. Interestingly, in pediatric subjects above 5 years, cerebrospinal fluid (CSF) levels of IGF-1 did not differ between affected and control subjects [93], while serum levels were reported to vary in children with ASD, with various age groups typically ranging from 4 to 12 years [167,169,170,171].
The timing of the suggested biphasic alteration in circulating IGF-1 level coincides well with the ASD-specific switch in growth rate compared to controls [56,172] in light of the observations that neonatal somatic growth correlated with circulating IGF-1 levels [118,173]. In infants with later ASD diagnosis, excess extraaxial volume [53] and brain volume [52] seem to stabilize after 12–18 months. Similarly, although imaging studies reported structural hyperconnectivity prior to 20 months, hypoconnectivity was observed in ASD patients by the age of 2–3 years [57,58]. Additionally, a particularly interesting analysis has found that white matter overgrowth in ASD is specific to myelinization processes starting within the first 4 months after birth [174]. Based on the well-described effect of IGF-1 on brain development [165], the timing of these brain myelination periods overlaps well with the proposed timing of early postnatal developmental dysregulations in ASD.
The most consistent non-gestational risk factors of ASD could also be explained by the concept of perinatal IGF-1 dysregulation. Boys bear approximately a 3-fold risk of ASD incidence compared to girls [32]. Male infants are known to be born larger [175,176], have higher brain volume [177,178], and undergo slightly faster postnatal brain growth than females [26,179]. The IGF-1 level of newborn boys, in contrast, is reported as lower or similar to that of girls, both in serum [118,180,181,182] and CSF [183], and female infants are more insulin-resistant than boys [176,184]. Therefore, male infants, since they display a higher growth rate with lower or similar IGF-1 levels and concomitant higher insulin sensitivity than females, might be more sensitive to insulin-regulated hypertrophy mediated by excess IGF-1 and its abrupt drop, and this amplification of IGF-1-mediated effects might be the cause of their susceptibility to ASD.
The single highest risk for idiopathic ASD is an affected sibling [30] with a clear familial connection [12] despite the lack of high-penetrance single-risk gene variants [14]. Genetic architecture clearly modifies the penetrance of neurodevelopmental disturbances and thus definitely exerts a contribution to the appearance of ASD that should not be underestimated. Still, according to the hypothesis above, the combined incidence of two groups of gestational conditions could be the specific etiologic trigger for idiopathic ASD, and therefore, careful separation of genetic influence from the influence of the intrauterine environment is required. The contribution of genetic and environmental factors is most often assessed by analyzing concordance in monozygotic and dizygotic twins [12]. However, as intrauterine growth restriction, as presented above, can be suspected in the etiology of ASD, such a heritability analysis has to take into account the difference in placental organization of dizygotic and monozygotic twins. Dizygotic twins are obligatory dichorionic; they possess separate placentas, while the majority of monozygotic twin pairs are monochorionic, sharing the same placenta. Placental dysfunction is a major contributor to fetal growth insufficiency and IUGR [185,186]; therefore, monochorionicity might lead to overestimation of heritability as impaired placental function might not be separated from genetic overlap. It has to be noted that preeclampsia, the strongest maternal risk factor of ASD and a risk factor for IUGR, parallels idiopathic ASD in its familial aggregation with yet unclear genetic etiology [187].
The steeply increasing prevalence of ASD can be partly related to the increased awareness and diagnostic tolerance of the behavioral spectrum, however, a true increase in prevalence is nevertheless suggested [4,188]. According to the etiologic hypothesis presented, the co-occurrence of maternal insulin resistance and risk factors of IUGR, like chronic or gestational hypertonia, is a prerequisite for biphasic neonatal metabolic dysregulation. While prevalence trends in gestational hypertensive disorders in the last decades are reported as varied [189,190,191], the prevalence of pregestational obesity and pregestational/gestational diabetes has been rising significantly since decades [192,193,194,195] in line with the steadily increasing ASD prevalence.
Finally, increasing maternal age, too, is unambiguously a risk factor for metabolic syndrome–by multiple definitions–in women of childbearing age in developed countries [196,197], and also for its various manifestations like gestational diabetes [192,198], overweight, or obesity [199]. Increasing maternal age, especially above 40 years [34], as a risk factor for ASD could therefore indirectly transmit the effect of these gestational conditions.

2.3. Prospective Testing of the Hypothesis

The etiologic hypothesis presented here attempts to add points for consideration in order to improve our understanding of this pervasive disorder. Numerous epidemiologic studies have investigated the contribution of environmental, gestational, and maternal conditions to ASD risk. The principle proposed above is that of a combination of gestational conditions leading to opposing postnatal growth dysregulation (e.g., maternal metabolic disturbances in combination with preeclampsia, gestational hypertension, or SSRI use). Therefore, investigation of combinations of these specific conditions in regard to ASD incidence risk would be interesting, as suggested in general in [35]. Additionally, as signs of maternal metabolic syndrome and insulin resistance are risk factors of ASD [42] and as gestational diabetes bears a significant risk of later type 2 diabetes of the mother, the apparent risk posed by having an ASD offspring on later maternal type 2 diabetes is worth investigating. Moreover, as discussed above, heritability studies on ASD prevalence in monozygotic twins taking chorionicity into account could improve the dissection of genetic and intrauterine environmental effects, further increasing our understanding of the role of genetic influence. Higher concordance in monochorionic compared to dichorionic twins would support the role of placental dysfunction in the latter incidence of ASD.
Finally, and perhaps most importantly, CSF IGF-1 level determination could be extended to at-risk neonates 3–6 months old in order to test the hypothesis of early elevated levels of this neurotrophic factor, as such an analysis has not been reported yet. The lower concentration of arginine vasopressin, but not oxytocin, in the CSF of 0–3-month-old neonates in high concordance with idiopathic ASD diagnosis in a quasi-prospective re-evaluated random population sample [200] supports that neonatal liquor composition could show a clear alteration in ASD from typical development at such an early age. If liquor IGF-1 levels are found elevated in affected neonates, pregestational dietary or antidiabetic interventions could be tested prospectively on ASD incidence.
Limitations of the above hypothesis lie in the lack of incorporation of the paracrine effects of IGF-1 or the complex contribution of IGFBPs, and the role of genetic predisposition in affecting the incidence and the phenotype of emerging ASD, factors that could serve as subjects of further research.

3. Conclusions

The most consistent maternal and gestational risk factors of idiopathic ASD overlap with two groups of gestational conditions affecting fetal growth through the insulin-IGF-1 axis. Asynchronous termination of these drivers after birth would lead to a biphasic postnatal dysregulation of IGF-1 tone in the infant, with the early insulin resistance-driven overgrowth in the first life year coinciding with the well-documented and specific accelerated development and growth in ASD. This excess IGF-1 spurt might lead to irreversible structural connectivity abnormalities specific to ASD, followed by slowed maturation of neurocircuits, resulting in lasting network dysfunction. If this concept wins confirmation in at-risk infants, in addition to providing new research avenues, it could support effective early prevention initiatives for this burdensome condition.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

The helpful suggestions and comments by Nóra Hartvig and András Boros and careful reading of the manuscript by Klára Felsővályi are acknowledged.

Conflicts of Interest

A.V. is an employee of Gedeon Richter Plc. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ADHDAttention deficit hyperactivity disorder
ASDAutism spectrum disorder
CSFCerebrospinal fluid
GHGrowth hormone
GHRHGrowth hormone releasing hormone
IGF-1Insulin-like growth factor 1
IGF-1RInsulin-like growth factor 1 receptor
IGFBPInsulin-like growth factor-binding protein
IUGRIntrauterine growth restriction
PCPurkinje cell
SGASmall for gestational age
SSRISelective serotonin reuptake inhibitor

References

  1. Lord, C.; Brugha, T.S.; Charman, T.; Cusack, J.; Dumas, G.; Frazier, T.; Jones, E.J.H.; Jones, R.M.; Pickles, A.; State, M.W.; et al. Autism Spectrum Disorder. Nat. Rev. Dis. Primers 2020, 6, 5. [Google Scholar] [CrossRef] [PubMed]
  2. Masi, A.; DeMayo, M.M.; Glozier, N.; Guastella, A.J. An Overview of Autism Spectrum Disorder, Heterogeneity and Treatment Options. Neurosci. Bull. 2017, 33, 183–193. [Google Scholar] [CrossRef] [PubMed]
  3. Rosen, N.E.; Lord, C.; Volkmar, F.R. The Diagnosis of Autism: From Kanner to DSM-III to DSM-5 and Beyond. J. Autism Dev. Disord. 2021, 51, 4253–4270. [Google Scholar] [CrossRef] [PubMed]
  4. Chiarotti, F.; Venerosi, A. Epidemiology of Autism Spectrum Disorders: A Review of Worldwide Prevalence Estimates since 2014. Brain Sci. 2020, 10, 274. [Google Scholar] [CrossRef]
  5. Zeidan, J.; Fombonne, E.; Scorah, J.; Ibrahim, A.; Durkin, M.S.; Saxena, S.; Yusuf, A.; Shih, A.; Elsabbagh, M. Global Prevalence of Autism: A Systematic Review Update. Autism Res. 2022, 15, 778–790. [Google Scholar] [CrossRef]
  6. Leigh, J.P.; Du, J. Brief Report: Forecasting the Economic Burden of Autism in 2015 and 2025 in the United States. J. Autism Dev. Disord. 2015, 45, 4135–4139. [Google Scholar] [CrossRef]
  7. Buescher, A.V.S.; Cidav, Z.; Knapp, M.; Mandell, D.S. Costs of Autism Spectrum Disorders in the United Kingdom and the United States. JAMA Pediatr. 2014, 168, 721–728. [Google Scholar] [CrossRef]
  8. Nordahl, C.W.; Andrews, D.S.; Dwyer, P.; Waizbard-Bartov, E.; Restrepo, B.; Lee, J.K.; Heath, B.; Saron, C.; Rivera, S.M.; Solomon, M.; et al. The Autism Phenome Project: Toward Identifying Clinically Meaningful Subgroups of Autism. Front. Neurosci. 2022, 15, 786220. [Google Scholar] [CrossRef]
  9. Amaral, D.G. Examining the Causes of Autism. Cerebrum 2017, 2017, cer-01-17. [Google Scholar]
  10. Hertz-Picciotto, I.; Schmidt, R.J.; Krakowiak, P. Understanding Environmental Contributions to Autism: Causal Concepts and the State of Science. Autism Res. 2018, 11, 554–586. [Google Scholar] [CrossRef]
  11. Hansen, S.N.; Schendel, D.E.; Francis, R.W.; Windham, G.C.; Bresnahan, M.; Levine, S.Z.; Reichenberg, A.; Gissler, M.; Kodesh, A.; Bai, D.; et al. Recurrence Risk of Autism in Siblings and Cousins: A Multinational, Population-Based Study. J. Am. Acad. Child. Adolesc. Psychiatry 2019, 58, 866–875. [Google Scholar] [CrossRef] [PubMed]
  12. Tick, B.; Bolton, P.; Happé, F.; Rutter, M.; Rijsdijk, F. Heritability of Autism Spectrum Disorders: A Meta-Analysis of Twin Studies. J. Child. Psychol. Psychiatry 2016, 57, 585–595. [Google Scholar] [CrossRef] [PubMed]
  13. Manoli, D.S.; State, M.W. Autism Spectrum Disorder Genetics and the Search for Pathological Mechanisms. Am. J. Psychiatry 2021, 178, 30–38. [Google Scholar] [CrossRef]
  14. Wiśniowiecka-Kowalnik, B.; Nowakowska, B.A. Genetics and Epigenetics of Autism Spectrum Disorder—Current Evidence in the Field. J. Appl. Genet. 2019, 60, 37–47. [Google Scholar] [CrossRef]
  15. Qiu, S.; Qiu, Y.; Li, Y.; Cong, X. Genetics of Autism Spectrum Disorder: An Umbrella Review of Systematic Reviews and Meta-Analyses. Transl. Psychiatry 2022, 12, 249. [Google Scholar] [CrossRef]
  16. Casanova, M.F.; Casanova, E.L.; Frye, R.E.; Baeza-Velasco, C.; LaSalle, J.M.; Hagerman, R.J.; Scherer, S.W.; Natowicz, M.R. Editorial: Secondary vs. Idiopathic Autism. Front. Psychiatry 2020, 11, 297. [Google Scholar] [CrossRef]
  17. van’t Hof, M.; Tisseur, C.; van Berckelear-Onnes, I.; van Nieuwenhuyzen, A.; Daniels, A.M.; Deen, M.; Hoek, H.W.; Ester, W.A. Age at Autism Spectrum Disorder Diagnosis: A Systematic Review and Meta-Analysis from 2012 to 2019. Autism 2021, 25, 862–873. [Google Scholar] [CrossRef]
  18. Ozonoff, S.; Iosif, A.-M.; Baguio, F.; Cook, I.C.; Moore Hill, M.; Hutman, T.; Rogers, S.J.; Rozga, A.; Sangha, S.; Sigman, M.; et al. A Prospective Study of the Emergence of Early Behavioral Signs of Autism. J. Am. Acad. Child. Adolesc. Psychiatry 2010, 49, 256–266. [Google Scholar] [CrossRef] [PubMed]
  19. Jones, E.J.H.; Venema, K.; Earl, R.; Lowy, R.; Barnes, K.; Estes, A.; Dawson, G.; Webb, S.J. Reduced Engagement with Social Stimuli in 6-Month-Old Infants with Later Autism Spectrum Disorder: A Longitudinal Prospective Study of Infants at High Familial Risk. J. Neurodev. Disord. 2016, 8, 7. [Google Scholar] [CrossRef]
  20. Gotham, K.; Pickles, A.; Lord, C. Trajectories of Autism Severity in Children Using Standardized ADOS Scores. Pediatrics 2012, 130, e1278. [Google Scholar] [CrossRef]
  21. Brian, J.; Bryson, S.E.; Smith, I.M.; Roberts, W.; Roncadin, C.; Szatmari, P.; Zwaigenbaum, L. Stability and Change in Autism Spectrum Disorder Diagnosis from Age 3 to Middle Childhood in a High-Risk Sibling Cohort. Autism 2016, 20, 888–892. [Google Scholar] [CrossRef] [PubMed]
  22. Kočovská, E.; Billstedt, E.; Ellefsen, A.; Kampmann, H.; Gillberg, I.C.; Biskupstø, R.; Andorsdóttir, G.; Stóra, T.; Minnis, H.; Gillberg, C. Autism in the Faroe Islands: Diagnostic Stability from Childhood to Early Adult Life. Scient World J. 2013, 2013, 592371. [Google Scholar] [CrossRef]
  23. Orm, S.; Andersen, P.N.; Fossum, I.N.; Øie, M.G.; Skogli, E.W. Brief Report: Autism Spectrum Disorder Diagnostic Persistence in a 10-Year Longitudinal Study. Res. Autism Spectr. Disord. 2022, 97, 102007. [Google Scholar] [CrossRef]
  24. Piven, J.; Elison, J.T.; Zylka, M.J. Toward a Conceptual Framework for Early Brain and Behavior Development in Autism. Mol. Psychiatry 2017, 22, 1385–1394. [Google Scholar] [CrossRef]
  25. Hazlett, H.C.; Gu, H.; Munsell, B.C.; Kim, S.H.; Styner, M.; Wolff, J.J.; Elison, J.T.; Swanson, M.R.; Zhu, H.; Botteron, K.N.; et al. Early Brain Development in Infants at High Risk for Autism Spectrum Disorder. Nature 2017, 542, 348–351. [Google Scholar] [CrossRef] [PubMed]
  26. Holland, D.; Chang, L.; Ernst, T.M.; Curran, M.; Buchthal, S.D.; Alicata, D.; Skranes, J.; Johansen, H.; Hernandez, A.; Yamakawa, R.; et al. Structural Growth Trajectories and Rates of Change in the First 3 Months of Infant Brain Development. JAMA Neurol. 2014, 71, 1266–1274. [Google Scholar] [CrossRef] [PubMed]
  27. Knickmeyer, R.C.; Gouttard, S.; Kang, C.; Evans, D.; Wilber, K.; Smith, J.K.; Hamer, R.M.; Lin, W.; Gerig, G.; Gilmore, J.H. A Structural MRI Study of Human Brain Development from Birth to 2 Years. J. Neurosci. 2008, 28, 12176–12182. [Google Scholar] [CrossRef]
  28. Amaral, D.G. The Promise and the Pitfalls of Autism Research: An Introductory Note for New Autism Researchers. Brain Res. 2011, 1380, 3–9. [Google Scholar] [CrossRef]
  29. Kim, J.Y.; Son, M.J.; Son, C.Y.; Radua, J.; Eisenhut, M.; Gressier, F.; Koyanagi, A.; Carvalho, A.F.; Stubbs, B.; Solmi, M.; et al. Environmental Risk Factors and Biomarkers for Autism Spectrum Disorder: An Umbrella Review of the Evidence. Lancet Psychiatry 2019, 6, 590–600. [Google Scholar] [CrossRef]
  30. Ozonoff, S.; Young, G.S.; Carter, A.; Messinger, D.; Yirmiya, N.; Zwaigenbaum, L.; Bryson, S.; Carver, L.J.; Constantino, J.N.; Dobkins, K.; et al. Recurrence Risk for Autism Spectrum Disorders: A Baby Siblings Research Consortium Study. Pediatrics 2011, 128, e488. [Google Scholar] [CrossRef]
  31. Maenner, M.J.; Shaw, K.A.; Bakian, A.V.; Bilder, D.A.; Durkin, M.S.; Esler, A.; Furnier, S.M.; Hallas, L.; Hall-Lande, J.; Hudson, A.; et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018. MMWR. Surveill. Summ. 2021, 70, 1–16. [Google Scholar] [CrossRef] [PubMed]
  32. Loomes, R.; Hull, L.; Polmear Locke Mandy, W. What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis. J. Am. Acad. Child. Adolesc. Psychiatry 2017, 56, 466–474. [Google Scholar] [CrossRef]
  33. Wu, S.; Wu, F.; Ding, Y.; Hou, J.; Bi, J.; Zhang, Z. Advanced Parental Age and Autism Risk in Children: A Systematic Review and Meta-Analysis. Acta Psychiatr. Scand. 2017, 135, 29–41. [Google Scholar] [CrossRef]
  34. Sandin, S.; Schendel, D.; Magnusson, P.; Hultman, C.; Surén, P.; Susser, E.; GrØnborg, T.; Gissler, M.; Gunnes, N.; Gross, R.; et al. Autism Risk Associated with Parental Age and with Increasing Difference in Age between the Parents. Mol. Psychiatry 2016, 21, 693–700. [Google Scholar] [CrossRef] [PubMed]
  35. Katz, J.; Reichenberg, A.; Kolevzon, A. Prenatal and Perinatal Metabolic Risk Factors for Autism: A Review and Integration of Findings from Population-Based Studies. Curr. Opin. Psychiatry 2021, 34, 94–104. [Google Scholar] [CrossRef] [PubMed]
  36. Dachew, B.A.; Mamun, A.; Maravilla, J.C.; Alati, R. Pre-Eclampsia and the Risk of Autism-Spectrum Disorder in Offspring: Meta-Analysis. Br. J. Psychiatry 2018, 212, 142–147. [Google Scholar] [CrossRef]
  37. Kaplan, Y.C.; Keskin-Arslan, E.; Acar, S.; Sozmen, K. Prenatal Selective Serotonin Reuptake Inhibitor Use and the Risk of Autism Spectrum Disorder in Children: A Systematic Review and Meta-Analysis. Reproductive Toxicology 2016, 66, 31–43. [Google Scholar] [CrossRef]
  38. Leshem, R.; Bar-Oz, B.; Diav-Citrin, O.; Gbaly, S.; Soliman, J.; Renoux, C.; Matok, I. Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Norepinephrine Reuptake Inhibitors (SNRIs) During Pregnancy and the Risk for Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) in the Offspring: A True Effect or a Bias? A Systematic Review & Meta-Analysis. Curr. Neuropharmacol. 2021, 19, 896–906. [Google Scholar] [CrossRef]
  39. Xu, G.; Jing, J.; Bowers, K.; Liu, B.; Bao, W. Maternal Diabetes and the Risk of Autism Spectrum Disorders in the Offspring: A Systematic Review and Meta-Analysis. J. Autism Dev. Disord. 2014, 44, 766–775. [Google Scholar] [CrossRef]
  40. Wang, C.; Geng, H.; Liu, W.; Zhang, G. Prenatal, Perinatal, and Postnatal Factors Associated with Autism: A Meta-Analysis. Medicine 2017, 96, e6696. [Google Scholar] [CrossRef]
  41. Rowland, J.; Wilson, C.A. The Association between Gestational Diabetes and ASD and ADHD: A Systematic Review and Meta-Analysis. Sci. Rep. 2021, 11, 5136. [Google Scholar] [CrossRef] [PubMed]
  42. Abraham, D.A.; Rajanandh, M.G. Does Metabolic Syndrome during Pregnancy Really a Risk to Autism Spectrum Disorder? Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 1591–1592. [Google Scholar] [CrossRef] [PubMed]
  43. Wang, Y.; Tang, S.; Xu, S.; Weng, S.; Liu, Z. Maternal Body Mass Index and Risk of Autism Spectrum Disorders in Offspring: A Meta-Analysis. Sci. Rep. 2016, 6, 34248. [Google Scholar] [CrossRef] [PubMed]
  44. Modabbernia, A.; Velthorst, E.; Reichenberg, A. Environmental Risk Factors for Autism: An Evidence-Based Review of Systematic Reviews and Meta-Analyses. Mol. Autism 2017, 8, 13. [Google Scholar] [CrossRef]
  45. Agrawal, S.; Rao, S.C.; Bulsara, M.K.; Patole, S.K. Prevalence of Autism Spectrum Disorder in Preterm Infants: A Meta-Analysis. Pediatrics 2018, 142, e20180134. [Google Scholar] [CrossRef]
  46. Ma, X.; Zhang, J.; Su, Y.; Lu, H.; Li, J.; Wang, L.; Shang, S.; Yue, W. Association of Birth Weight with Risk of Autism: A Systematic Review and Meta-Analysis. Res. Autism Spectr. Disord. 2022, 92, 101934. [Google Scholar] [CrossRef]
  47. Campbell, D.J.; Chang, J.; Chawarska, K. Early Generalized Overgrowth in Autism Spectrum Disorder: Prevalence Rates, Gender Effects, and Clinical Outcomes. J. Am. Acad. Child. Adolesc. Psychiatry 2014, 53, 1063–1073.e5. [Google Scholar] [CrossRef]
  48. Chawarska, K.; Campbell, D.; Chen, L.; Shic, F.; Klin, A.; Chang, J. Early Generalized Overgrowth in Boys with Autism. Arch. Gen. Psychiatry 2011, 68, 1021–1031. [Google Scholar] [CrossRef]
  49. Cárdenas-de-la-Parra, A.; Lewis, J.D.; Fonov, V.S.; Botteron, K.N.; McKinstry, R.C.; Gerig, G.; Pruett, J.R.; Dager, S.R.; Elison, J.T.; Styner, M.A.; et al. A Voxel-Wise Assessment of Growth Differences in Infants Developing Autism Spectrum Disorder. Neuroimage Clin. 2021, 29, 102551. [Google Scholar] [CrossRef]
  50. Sacco, R.; Gabriele, S.; Persico, A.M. Head Circumference and Brain Size in Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Psychiatry Res. Neuroimaging 2015, 234, 239–251. [Google Scholar] [CrossRef]
  51. Courchesne, E.; Carper, R.; Akshoomoff, N. Evidence of Brain Overgrowth in the First Year of Life in Autism. J. Am. Med. Assoc. 2003, 290, 337–344. [Google Scholar] [CrossRef] [PubMed]
  52. Hazlett, H.C.; Poe, M.D.; Gerig, G.; Styner, M.; Chappell, C.; Smith, R.G.; Vachet, C.; Piven, J. Early Brain Overgrowth in Autism Associated With an Increase in Cortical Surface Area Before Age 2 Years. Arch. Gen. Psychiatry 2011, 68, 467–476. [Google Scholar] [CrossRef]
  53. Shen, M.D.; Nordahl, C.W.; Li, D.D.; Lee, A.; Angkustsiri, K.; Emerson, R.W.; Rogers, S.J.; Ozonoff, S.; Amaral, D.G. Extra-Axial Cerebrospinal Fluid in High-Risk and Normal-Risk Children with Autism Aged 2–4 Years: A Case-Control Study. Lancet Psychiatry 2018, 5, 895–904. [Google Scholar] [CrossRef]
  54. Surén, P.; Stoltenberg, C.; Bresnahan, M.; Hirtz, D.; Lie, K.K.; Lipkin, W.I.; Magnus, P.; Reichborn-Kjennerud, T.; Schjølberg, S.; Susser, E.; et al. Early Growth Patterns in Children with Autism. Epidemiology 2013, 24, 660–670. [Google Scholar] [CrossRef] [PubMed]
  55. Lange, N.; Travers, B.G.; Bigler, E.D.; Prigge, M.B.D.; Froehlich, A.L.; Nielsen, J.A.; Cariello, A.N.; Zielinski, B.A.; Anderson, J.S.; Fletcher, P.T.; et al. Longitudinal Volumetric Brain Changes in Autism Spectrum Disorder Ages 6–35 Years. Autism Res. 2015, 8, 82–93. [Google Scholar] [CrossRef] [PubMed]
  56. Conti, E.; Calderoni, S.; Marchi, V.; Muratori, F.; Cioni, G.; Guzzetta, A. The First 1000 Days of the Autistic Brain: A Systematic Review of Diffusion Imaging Studies. Front. Hum. Neurosci. 2015, 9, 159. [Google Scholar] [CrossRef]
  57. Wolff, J.J.; Gu, H.; Gerig, G.; Elison, J.T.; Styner, M.; Gouttard, S.; Botteron, K.N.; Dager, S.R.; Dawson, G.; Estes, A.M.; et al. Differences in White Matter Fiber Tract Development Present from 6 to 24 Months in Infants with Autism. Am. J. Psychiatry 2012, 169, 589–600. [Google Scholar] [CrossRef]
  58. Solso, S.; Xu, R.; Proudfoot, J.; Hagler, D.J.; Campbell, K.; Venkatraman, V.; Carter Barnes, C.; Ahrens-Barbeau, C.; Pierce, K.; Dale, A.; et al. Diffusion Tensor Imaging Provides Evidence of Possible Axonal Overconnectivity in Frontal Lobes in Autism Spectrum Disorder Toddlers. Biol. Psychiatry 2016, 79, 676–684. [Google Scholar] [CrossRef]
  59. Heinonen, K.; Räikkönen, K.; Pesonen, A.K.; Andersson, S.; Kajantie, E.; Eriksson, J.G.; Vartia, T.; Wolke, D.; Lano, A. Trajectories of Growth and Symptoms of Attention-Deficit/Hyperactivity Disorder in Children: A Longitudinal Study. BMC Pediatr. 2011, 11, 84. [Google Scholar] [CrossRef]
  60. Ferrer, M.; García-Esteban, R.; Iñiguez, C.; Costa, O.; Fernández-Somoano, A.; Rodríguez-Delhi, C.; Ibarluzea, J.; Lertxundi, A.; Tonne, C.; Sunyer, J.; et al. Head Circumference and Child ADHD Symptoms and Cognitive Functioning: Results from a Large Population-Based Cohort Study. Eur. Child. Adolesc. Psychiatry 2019, 28, 377–388. [Google Scholar] [CrossRef]
  61. Xiao, Z.; Qiu, T.; Ke, X.; Xiao, X.; Xiao, T.; Liang, F.; Zou, B.; Huang, H.; Fang, H.; Chu, K.; et al. Autism Spectrum Disorder as Early Neurodevelopmental Disorder: Evidence from the Brain Imaging Abnormalities in 2–3 Years Old Toddlers. J. Autism Dev. Disord. 2014, 44, 1633–1640. [Google Scholar] [CrossRef] [PubMed]
  62. Constantino, J.N.; Charman, T. Diagnosis of Autism Spectrum Disorder: Reconciling the Syndrome, Its Diverse Origins, and Variation in Expression. Lancet Neurol. 2016, 15, 279–291. [Google Scholar] [CrossRef] [PubMed]
  63. Bailey, A.; Luthert, P.; Dean, A.; Harding, B.; Janota, I.; Montgomery, M.; Rutter, M.; Lantos, P. A Clinicopathological Study of Autism. Brain 1998, 121, 889–905. [Google Scholar] [CrossRef]
  64. Pretzsch, C.M.; Ecker, C. The neuroanatomy of autism. In The Neuroscience of Autism, 2nd ed.; Kana, R.K., Ed.; Elsevier: Amsterdam, The Netherlands, 2022; pp. 87–105. [Google Scholar] [CrossRef]
  65. Palmen, S.J.M.C.; Van Engeland, H.; Hof, P.R.; Schmitz, C. Neuropathological Findings in Autism. Brain 2004, 127, 2572–2583. [Google Scholar] [CrossRef] [PubMed]
  66. Donovan, A.P.A.; Basson, M.A. The Neuroanatomy of Autism—A Developmental Perspective. J. Anat. 2017, 230, 4–15. [Google Scholar] [CrossRef]
  67. Darwish, M.; El Hajj, R.; Khayat, L.; Alaaeddine, N. Stem Cell Secretions as a Potential Therapeutic Agent for Autism Spectrum Disorder: A Narrative Review. Stem Cell Rev. Rep. 2024, 20, 1252–1272. [Google Scholar] [CrossRef]
  68. Stoner, R.; Chow, M.L.; Boyle, M.P.; Sunkin, S.M.; Mouton, P.R.; Roy, S.; Wynshaw-Boris, A.; Colamarino, S.A.; Lein, E.S.; Courchesne, E. Patches of Disorganization in the Neocortex of Children with Autism. N. Eng. J. Med. 2014, 370, 1209–1219. [Google Scholar] [CrossRef]
  69. Wegiel, J.; Kuchna, I.; Nowicki, K.; Imaki, H.; Wegiel, J.; Marchi, E.; Ma, S.Y.; Chauhan, A.; Chauhan, V.; Bobrowicz, T.W.; et al. The Neuropathology of Autism: Defects of Neurogenesis and Neuronal Migration, and Dysplastic Changes. Acta Neuropathol. 2010, 119, 755–770. [Google Scholar] [CrossRef]
  70. Wegiel, J.; Flory, M.; Kuchna, I.; Nowicki, K.; Ma, S.Y.; Imaki, H.; Wegiel, J.; Cohen, I.L.; London, E.; Wisniewski, T.; et al. Stereological Study of the Neuronal Number and Volume of 38 Brain Subdivisions of Subjects Diagnosed with Autism Reveals Significant Alterations Restricted to the Striatum, Amygdala and Cerebellum. Acta Neuropathol. Commun. 2014, 2, 141. [Google Scholar] [CrossRef]
  71. Skefos, J.; Cummings, C.; Enzer, K.; Holiday, J.; Weed, K.; Levy, E.; Yuce, T.; Kemper, T.; Bauman, M. Regional Alterations in Purkinje Cell Density in Patients with Autism. PLoS ONE 2014, 9, e81255. [Google Scholar] [CrossRef]
  72. Kemper, T.L.; Bauman, M.L. The Contribution of Neuropathologic Studies to the Understanding of Autism. Neurology Clin. 1993, 11, 175–187. [Google Scholar] [CrossRef]
  73. Whitney, E.R.; Kemper, T.L.; Rosene, D.L.; Bauman, M.L.; Blatt, G.J. Density of Cerebellar Basket and Stellate Cells in Autism: Evidence for a Late Developmental Loss of Purkinje Cells. J. Neurosci. Res. 2009, 87, 2245–2254. [Google Scholar] [CrossRef] [PubMed]
  74. Triarhou, L.C.; Ghetti, B. Stabilisation of Neurone Number in the Inferior Olivary Complex of Aged “Purkinje Cell Degeneration” Mutant Mice. Acta Neuropathol. 1991, 81, 597–602. [Google Scholar] [CrossRef] [PubMed]
  75. Zanjani, H.; Herrup, K.; Mariani, J. Cell Number in the Inferior Olive of Nervous and Leaner Mutant Mice. J. Neurogenet. 2004, 18, 327–339. [Google Scholar] [CrossRef]
  76. Heckroth, J.A.; Abbott, L.C. Purkinje Cell Loss from Alternating Sagittal Zones in the Cerebellum of Leaner Mutant Mice. Brain Res. 1994, 658, 93–104. [Google Scholar] [CrossRef]
  77. Holmes, G. On the Connections of the Inferior Olives with the Cerebellum in Man. Brain 1908, 31, 125–137. [Google Scholar] [CrossRef]
  78. Louis, E.D.; Babij, R.; Cortés, E.; Vonsattel, J.P.G.; Faust, P.L. The Inferior Olivary Nucleus: A Postmortem Study of Essential Tremor Cases versus Controls. Movement Disord. 2013, 28, 779–786. [Google Scholar] [CrossRef]
  79. Williams, R.S.; Hauser, S.L.; Purpura, D.P.; Delong, G.R.; Swisher, C.N. Autism and Mental Retardation Neuropathologic Studies Performed in Four Retarded Persons with Autistic Behavior. Arch. Neurol. 1980, 34, 749–753. [Google Scholar] [CrossRef]
  80. Mavroudis, I.; Petridis, F.; Kazis, D.; Njau, S.N.; Costa, V.; Baloyannis, S.J. Purkinje Cells Pathology in Alzheimer’s Disease. Am. J. Alzheimers Dis. Other Demen 2019, 34, 439–449. [Google Scholar] [CrossRef]
  81. Chaudhari, K.; Wang, L.; Kruse, J.; Winters, A.; Sumien, N.; Shetty, R.; Prah, J.; Liu, R.; Shi, J.; Forster, M.; et al. Early Loss of Cerebellar Purkinje Cells in Human and a Transgenic Mouse Model of Alzheimer’s Disease. Neurol. Res. 2021, 43, 570–581. [Google Scholar] [CrossRef]
  82. Park, E.; Mcknight, S.; Ai, J.; Baker, A.J. Purkinje Cell Vulnerability to Mild and Severe Forebrain Head Trauma. J. Neuropathol. Exp. Neurol. 2006, 65, 226–234. [Google Scholar] [CrossRef] [PubMed]
  83. Torres-Aleman, I.; Pons, S.; Santos-Benito, F.F. Survival of Purkinje Cells in Cerebellar Cultures Is Increased by Insulin-like Growth Factor I. Eur. J. Neurosci. 1992, 4, 864–869. [Google Scholar] [CrossRef]
  84. Croci, L.; Barili, V.; Chia, D.; Massimino, L.; Van Vugt, R.; Masserdotti, G.; Longhi, R.; Rotwein, P.; Consalez, G.G. Local Insulin-like Growth Factor I Expression Is Essential for Purkinje Neuron Survival at Birth. Cell Death Differ. 2011, 18, 48–59. [Google Scholar] [CrossRef]
  85. Fukudome, Y.; Tabata, T.; Miyoshi, T.; Haruki, S.; Araishi, K.; Sawada, S.; Kano, M. Insulin-like Growth Factor-I as a Promoting Factor for Cerebellar Purkinje Cell Development. Eur. J. Neurosci. 2003, 17, 2006–2016. [Google Scholar] [CrossRef] [PubMed]
  86. Tolbert, D.L.; Clark, B.R. GDNF and IGF-I Trophic Factors Delay Hereditary Purkinje Cell Degeneration and the Progression of Gait Ataxia. Exp. Neurol. 2003, 183, 205–219. [Google Scholar] [CrossRef] [PubMed]
  87. Bitoun, E.; Finelli, M.J.; Oliver, P.L.; Lee, S.; Davies, D.K.E. AF4 Is a Critical Regulator of the IGF-1 Signaling Pathway during Purkinje Cell Development. J. Neurosci. 2009, 29, 15366–15374. [Google Scholar] [CrossRef]
  88. Mount, H.T.J.; Dreyfus, C.F.; Back, I.B. Neurotrophin-3 Selectively Increases Cultured Purkinje Cell Survival. Neuroreport 1994, 5, 2497–2500. [Google Scholar] [CrossRef]
  89. Ghoumari, A.M.; Wehrlé, R.; De Zeeuw, C.I.; Sotelo, C.; Dusart, I. Inhibition of Protein Kinase C Prevents Purkinje Cell Death but Does Not Affect Axonal Regeneration. J. Neurosci. 2002, 22, 3531–3542. [Google Scholar] [CrossRef]
  90. Morrison, M.E.; Mason, C.A. Granule Neuron Regulation of Purkinje Cell Development: Striking a Balance Between Neurotrophin and Glutamate Signaling. J. Neurosci. 1998, 18, 3563–3573. [Google Scholar] [CrossRef]
  91. Mount, H.T.J.; Dean, D.O.; Alberch, J.; Dreyfus, C.F.; Black, I.B. Glial Cell Line-Derived Neurotrophic Factor Promotes the Survival and Morphologic Differentiation of Purkinje Cells. Proc. Natl. Acad. Sci. USA 1995, 92, 9092–9096. [Google Scholar] [CrossRef]
  92. Vanhala, R.; Turpeinen, U.; Riikonen, R. Low Levels of Insulin-like Growth Factor-I in Cerebrospinal Fluid in Children with Autism. Dev. Med. Child. Neurol. 2001, 43, 614–616. [Google Scholar] [CrossRef]
  93. Riikonen, R.; Makkonen, I.; Vanhala, R.; Turpeinen, U.; Kuikka, J.; Kokki, H. Cerebrospinal Fluid Insulin-like Growth Factors IGF-1 and IGF-2 in Infantile Autism. Dev. Med. Child. Neurol. 2006, 48, 751. [Google Scholar] [CrossRef] [PubMed]
  94. Riikonen, R.; Vanhala, R. Levels of Cerebrospinal Fluid Nerve-Growth Factor Differ in Infantile Autism and Rett Syndrome. Dev. Med. Child. Neurol. 1999, 41, 148–152. [Google Scholar] [CrossRef] [PubMed]
  95. Riikonen, R. Insulin-like Growth Factors in the Pathogenesis of Neurological Diseases in Children. Int. J. Mol. Sci. 2017, 18, 2056. [Google Scholar] [CrossRef] [PubMed]
  96. Bou Khalil, R. Insulin-Growth-Factor-1 (IGF-1): Just a Few Steps behind the Evidence in Treating Schizophrenia and/or Autism. CNS Spectr. 2019, 24, 277–278. [Google Scholar] [CrossRef]
  97. Steinman, G. IGF—Autism Prevention/Amelioration. Med. Hypotheses 2019, 122, 45–47. [Google Scholar] [CrossRef]
  98. Riikonen, R. Treatment of Autistic Spectrum Disorder with Insulin-like Growth Factors. Eur. J. Paediatr. Neurol. 2016, 20, 816–823. [Google Scholar] [CrossRef]
  99. Costales, J.; Kolevzon, A. The Therapeutic Potential of Insulin-like Growth Factor-1 in Central Nervous System Disorders. Neurosci. Biobehav. Rev. 2016, 63, 207–222. [Google Scholar] [CrossRef]
  100. Kolevzon, A.; Breen, M.S.; Siper, P.M.; Halpern, D.; Frank, Y.; Rieger, H.; Weismann, J.; Trelles, M.P.; Lerman, B.; Rapaport, R.; et al. Clinical Trial of Insulin-like Growth Factor-1 in Phelan-McDermid Syndrome. Mol. Autism 2022, 13, 17. [Google Scholar] [CrossRef]
  101. O’Leary, H.M.; Kaufmann, W.E.; Barnes, K.V.; Rakesh, K.; Kapur, K.; Tarquinio, D.C.; Cantwell, N.G.; Roche, K.J.; Rose, S.A.; Walco, A.C.; et al. Placebo-controlled Crossover Assessment of Mecasermin for the Treatment of Rett Syndrome. Ann. Clin. Transl. Neurol. 2018, 5, 323–332. [Google Scholar] [CrossRef]
  102. Neul, J.L.; Percy, A.K.; Benke, T.A.; Berry-Kravis, E.M.; Glaze, D.G.; Marsh, E.D.; Lin, T.; Stankovic, S.; Bishop, K.M.; Youakim, J.M. Trofinetide for the Treatment of Rett Syndrome: A Randomized Phase 3 Study. Nat. Med. 2023, 29, 1468–1475. [Google Scholar] [CrossRef] [PubMed]
  103. Berry-Kravis, E.; Horrigan, J.P.; Tartaglia, N.; Hagerman, R.; Kolevzon, A.; Erickson, C.A.; Hatti, S.; Snape, M.; Yaroshinsky, A.; Stoms, G.; et al. A Double-Blind, Randomized, Placebo-Controlled Clinical Study of Trofinetide in the Treatment of Fragile X Syndrome. Pediatr. Neurol. 2020, 110, 30–41. [Google Scholar] [CrossRef]
  104. Cheng, C.M.; Joncas, G.; Reinhardt, R.R.; Farrer, R.; Quarles, R.; Janssen, J.; Mcdonald, M.P.; Crawley, J.N.; Powell-Braxton, L.; Bondy, C.A. Biochemical and Morphometric Analyses Show That Myelination in the Insulin-like Growth Factor 1 Null Brain Is Proportionate to Its Neuronal Composition. J. Neurosci. 1998, 18, 5673–5681. [Google Scholar] [CrossRef] [PubMed]
  105. Pulford, B.E.; Ishii, D.N. Uptake of Circulating Insulin-Like Growth Factors (IGFs) into Cerebrospinal Fluid Appears to Be Independent of the IGF Receptors as Well as IGF-Binding Proteins. Endocrinology 2001, 142, 213–220. [Google Scholar] [CrossRef]
  106. Pan, W.; Kastin, A.J. Interactions of IGF-1 with the Blood-Brain Barrier in Vivo and in Situ. Neuroendocrinology 2000, 72, 171–178. [Google Scholar] [CrossRef] [PubMed]
  107. Jones, J.I.; Clemmons, D.R. Insulin-Like Growth Factors and Their Binding Proteins: Biological Actions. Endocr. Rev. 1995, 16, 3–34. [Google Scholar] [CrossRef]
  108. Yakar, S.; Liu, J.-L.; Stannard, B.; Butler, A.; Accili, D.; Sauer, B.; Leroith, D. Normal Growth and Development in the Absence of Hepatic Insulin-like Growth Factor I. Proc. Natl. Acad. Sci. USA 1999, 96, 7324–7329. [Google Scholar] [CrossRef]
  109. Wu, Y.; Sun, H.; Yakar, S.; LeRoith, D. Elevated Levels of Insulin-like Growth Factor (IGF)-I in Serum Rescue the Severe Growth Retardation of IGF-I Null Mice. Endocrinology 2009, 150, 4395–4403. [Google Scholar] [CrossRef]
  110. Stratikopoulos, E.; Szabolcs, M.; Dragatsis, I.; Klinakis, A.; Efstratiadis, A. The Hormonal Action of IGF1 in Postnatal Mouse Growth. Proc. Natl. Acad. Sci. USA 2008, 105, 19378–19383. [Google Scholar] [CrossRef]
  111. Murray, P.G.; Clayton, P.E. Endocrine Control of Growth. Am. J. Med. Genet. C Semin. Med. Genet. 2013, 163, 76–85. [Google Scholar] [CrossRef]
  112. Uhlén, M.; Fagerberg, L.; Hallström, B.M.; Lindskog, C.; Oksvold, P.; Mardinoglu, A.; Sivertsson, Å.; Kampf, C.; Sjöstedt, E.; Asplund, A.; et al. Tissue-Based Map of the Human Proteome. Science (1979) 2015, 347, 1260419. [Google Scholar] [CrossRef]
  113. Oliver, M.H.; Harding, J.E.; Breier, B.H.; Gluckman, P.D. Fetal Insulin-like Growth Factor (IGF)-I and IGF-II Are Regulated Differently by Glucose or Insulin in the Sheep Fetus. Reprod. Fertil. Dev. 1996, 8, 167–172. [Google Scholar] [CrossRef] [PubMed]
  114. Fowden, A.L. The Insulin-like Growth Factors and Feto-Placental Growth. Placenta 2003, 24, 803–812. [Google Scholar] [CrossRef] [PubMed]
  115. Mehta, A.; Hindmarsh, P.C.; Stanhope, R.G.; Turton, J.P.G.; Cole, T.J.; Preece, M.A.; Dattani, M.T. The Role of Growth Hormone in Determining Birth Size and Early Postnatal Growth, Using Congenital Growth Hormone Deficiency (GHD) as a Model. Clin. Endocrinol. 2005, 63, 223–231. [Google Scholar] [CrossRef] [PubMed]
  116. Dattani, M.T.; Malhotra, N. A Review of Growth Hormone Deficiency. Paediatr. Child. Health 2019, 29, 285–292. [Google Scholar] [CrossRef]
  117. Ong, K.K.; Langkamp, M.; Ranke, M.B.; Whitehead, K.; Hughes, I.A.; Acerini, C.L.; Dunger, D.B. Insulin-like Growth Factor I Concentrations in Infancy Predict Differential Gains in Body Length and Adiposity: The Cambridge Baby Growth Study. Am. J. Clin. Nutr. 2009, 90, 156–161. [Google Scholar] [CrossRef]
  118. Wang, X.; Xing, K.H.; Qi, J.; Guan, Y.; Zhang, J. Analysis of the Relationship of Insulin-like Growth Factor-1 to the Growth Velocity and Feeding of Healthy Infants. Growth Horm. IGF Res. 2013, 23, 215–219. [Google Scholar] [CrossRef]
  119. Skalkidou, A.; Petridou, E.; Papathoma, E.; Salvanos, H.; Trichopoulos, D. Growth Velocity during the First Postnatal Week of Life Is Linked to a Spurt of IGF-I Effect. Paediatr. Perinat. Epidemiol. 2003, 17, 281–286. [Google Scholar] [CrossRef]
  120. Iñiguez, G.; Ong, K.; Bazaes, R.; Avila, A.; Salazar, T.; Dunger, D.; Mericq, V. Longitudinal Changes in Insulin-like Growth Factor-I, Insulin Sensitivity, and Secretion from Birth to Age Three Years in Small-for-Gestational-Age Children. J. Clin. Endocrinol. Metabol. 2006, 91, 4645–4649. [Google Scholar] [CrossRef]
  121. Simmons, D. Interrelation between Umbilical Cord Serum Sex Hormones, Sex Hormone-Binding Globulin, Insulin-like Growth Factor I, and Insulin in Neonates from Normal Pregnancies and Pregnancies Complicated by Diabetes. J. Clin. Endocrinol. Metab. 1995, 80, 2217–2221. [Google Scholar] [CrossRef]
  122. Leger, J.; Noel, M.; Limal, J.M.; Czernichow, P. Growth Factors and Intrauterine Growth Retardation. II. Serum Growth Hormone, Insulin-like Growth Factor (IGF) I, and IGF-Binding Protein 3 Levels in Children with Intrauterine Growth Retardation Compared with Normal Control Subjects: Prospective Study from Birth to Two Years of Age. Study Group of IUGR. Pediatr. Res. 1996, 40, 101–107. [Google Scholar] [CrossRef] [PubMed]
  123. Low, L.C.K.; Tam, S.Y.M.; Kwan, E.Y.W.; Tsang, A.M.C.; Karlberg, J. Onset of Significant GH Dependence of Serum IGF-I and IGF-Binding Protein 3 Concentrations in Early Life. Pediatr. Res. 2001, 50, 736–742. [Google Scholar] [CrossRef] [PubMed]
  124. Michaelsen, K. Effect of Protein Intake from 6 to 24 Months On-like Growth Factor 1 (IGF-1) Levels, Body, Linear Growth Velocity, and Linear Growth: What Are the Implications for stunting and Wasting? Food Nutr. Bull. 2013, 34, 268–271. [Google Scholar] [CrossRef] [PubMed]
  125. Ibáñez, L.; Sebastiani, G.; Lopez-Bermejo, A.; Díaz, M.; Gómez-Roig, M.D.; De Zegher, F. Gender Specificity of Body Adiposity and Circulating Adiponectin, Visfatin, Insulin, and Insulin Growth Factor-I at Term Birth: Relation to Prenatal Growth. J. Clin. Endocrinol. Metabol. 2008, 93, 2774–2778. [Google Scholar] [CrossRef]
  126. Beltrand, J.; Nicolescu, R.; Kaguelidou, F.; Verkauskiene, R.; Sibony, O.; Chevenne, D.; Claris, O.; Lévy-Marchal, C. Catch-up Growth Following Fetal Growth Restriction Promotes Rapid Restoration of Fat Mass but without Metabolic Consequences at One Year of Age. PLoS ONE 2009, 4, e5343. [Google Scholar] [CrossRef] [PubMed]
  127. Mericq, V.; Ong, K.K.; Bazaes, R.; Peña, V.; Avila, A.; Salazar, T.; Soto, N.; Iñiguez, G.; Dunger, D.B. Longitudinal Changes in Insulin Sensitivity and Secretion from Birth to Age Three Years in Small- and Appropriate-for-Gestational-Age Children. Diabetol. 2005, 48, 2609–2614. [Google Scholar] [CrossRef]
  128. Albertsson-Wikland, K.; Karlberg, J. Natural Growth in Children Born Small for Gestational Age with and without Catch-up Growth. Acta Paediatr. 1994, 83, 64–70. [Google Scholar] [CrossRef]
  129. Hokken-Koelega, A.C.S.; De Ridder, M.A.J.; Lemmen, R.J.; Hartog, H.D.; De Muinck Keizer-Schrama, S.M.P.F.; Drop, S.L.S. Children Born Small for Gestational Age: Do They Catch Up? Pediatr. Res. 1995, 38, 267–271. [Google Scholar] [CrossRef]
  130. Ibáñez, L.; López-Bermejo, A.; Díaz, M.; Marcos, M.V.; Casano, P.; De Zegher, F. Abdominal Fat Partitioning and High-Molecular-Weight Adiponectin in Short Children Born Small for Gestational Age. J. Clin. Endocrinol. Metabol. 2009, 94, 1049–1052. [Google Scholar] [CrossRef]
  131. Geremia, C.; Cianfarani, S. Insulin Sensitivity in Children Born Small for Gestational Age (SGA). Rev. Diabet. Stud. 2004, 1, 58. [Google Scholar] [CrossRef]
  132. Chiesa, C.; Osborn, J.F.; Haass, C.; Natale, F.; Spinelli, M.; Scapillati, E.; Spinelli, A.; Pacifico, L. Ghrelin, Leptin, IGF-1, IGFBP-3, and Insulin Concentrations at Birth: Is There a Relationship with Fetal Growth and Neonatal Anthropometry? Clin. Chem. 2008, 54, 550–558. [Google Scholar] [CrossRef]
  133. Wolf, H.J.; Ebenbichler, C.F.; Huter, O.; Bodner, J.; Lechleitner, M.; Föger, B.; Patsch, J.R.; Desoye, G. Fetal Leptin and Insulin Levels Only Correlate in Large-for-Gestational Age Infants. Eur. J. Endocrinol. 2000, 142, 623–629. [Google Scholar] [CrossRef] [PubMed]
  134. Yalinbas, E.; Binay, C.; Simsek, E.; Aksit, M. The Role of Umbilical Cord Blood Concentration of IGF-I, IGF-II, Leptin, Adiponectin, Ghrelin, Resistin, and Visfatin in Fetal Growth. Am. J. Perinatol. 2019, 36, 600–608. [Google Scholar] [CrossRef] [PubMed]
  135. Christou, H.; Connors, J.M.; Ziotopoulou, M.; Hatzidakis, V.; Papathanassoglou, E.; Ringer, S.A.; Mantzoros, C.S. Cord Blood Leptin and Insulin-like Growth Factor Levels Are Independent Predictors of Fetal Growth. J. Clin. Endocrinol. Metabol. 2001, 86, 935–938. [Google Scholar] [CrossRef] [PubMed]
  136. Simental-Mendía, L.E.; Castañeda-Chacón, A.; Rodríguez-Morán, M.; Guerrero-Romero, F. Birth-Weight, Insulin Levels, and HOMA-IR in Newborns at Term. BMC Pediatr. 2012, 12, 94. [Google Scholar] [CrossRef]
  137. Cekmez, F.; Canpolat, F.E.; Pirgon, O.; Çetinkaya, M.; Aydinoz, S.; Suleymanoglu, S.; Ipcioglu, O.M.; Sarici, S.U. Apelin, Vaspin, Visfatin and Adiponectin in Large for Gestational Age Infants with Insulin Resistance. Cytokine 2011, 56, 387–391. [Google Scholar] [CrossRef]
  138. Chiavaroli, V.; Cutfield, W.S.; Derraik, J.G.B.; Pan, Z.; Ngo, S.; Sheppard, A.; Craigie, S.; Stone, P.; Sadler, L.; Ahlsson, F. Infants Born Large-for-Gestational-Age Display Slower Growth in Early Infancy, but No Epigenetic Changes at Birth. Sci. Rep. 2015, 5, 14540. [Google Scholar] [CrossRef]
  139. Taal, H.R.; Vd Heijden, A.J.; Steegers, E.A.P.; Hofman, A.; Jaddoe, V.W.V. Small and Large Size for Gestational Age at Birth, Infant Growth, and Childhood Overweight. Obesity 2013, 21, 1261–1268. [Google Scholar] [CrossRef]
  140. Dunn, R.K.; Uhing, M.; Goday, P.S. Catch-down Growth in Infants Born Large for Gestational Age. Nutr. Clin. Pract. 2021, 36, 1215–1219. [Google Scholar] [CrossRef]
  141. Ohkawa, N.; Shoji, H.; Ikeda, N.; Suganuma, H.; Shimizu, T. Relationship between Insulin-like Growth Factor 1, Leptin and Ghrelin Levels and Catch-up Growth in Small for Gestational Age Infants of 27–31 Weeks during Neonatal Intensive Care Unit Admission. J. Paediatr. Child. Health 2017, 53, 62–67. [Google Scholar] [CrossRef]
  142. Özkan, H.; Aydın, A.; Demir, N.; Erci, T.; Büyükgebiz, A. Associations of IGF-I, IGFBP-1 and IGFBP-3 on Intrauterine Growth and Early Catch-Up Growth. Biol. Neonate 1999, 76, 274–282. [Google Scholar] [CrossRef] [PubMed]
  143. Zhao, X.; Liu, Q.; Cao, S.; Pang, J.; Zhang, H.; Feng, T.; Deng, Y.; Yao, J.; Li, H. A Meta-Analysis of Selective Serotonin Reuptake Inhibitors (SSRIs) Use during Prenatal Depression and Risk of Low Birth Weight and Small for Gestational Age. J. Affect. Disord. 2018, 241, 563–570. [Google Scholar] [CrossRef] [PubMed]
  144. Toh, S.; Mitchell, A.A.; Louik, C.; Werler, M.M.; Chambers, C.D.; Hernández-Díaz, S. Antidepressant Use during Pregnancy and the Risk of Preterm Delivery and Fetal Growth Restriction. J. Clin. Psychopharmacol. 2009, 29, 555–560. [Google Scholar] [CrossRef]
  145. Takahashi, M.; Makino, S.; Oguma, K.; Imai, H.; Takamizu, A.; Koizumi, A.; Yoshida, K. Fetal Growth Restriction as the Initial Finding of Preeclampsia Is a Clinical Predictor of Maternal and Neonatal Prognoses: A Single-Center Retrospective Study. BMC Pregnancy Childbirth 2021, 21, 678. [Google Scholar] [CrossRef]
  146. Srinivas, S.K.; Edlow, A.G.; Neff, P.M.; Sammel, M.D.; Andrela, C.M.; Elovitz, M.A. Rethinking IUGR in Preeclampsia: Dependent or Independent of Maternal Hypertension? J. Perinatol. 2009, 29, 680–684. [Google Scholar] [CrossRef]
  147. Zhang, H.-G.; Yang, L.; Qiao, Z.-X.; Guo, W. Effect of Gestational Hypertension on Fetal Growth Restriction, Endocrine and Cardiovascular Disorders. Asian J. Surg. 2022, 45, 1048–1049. [Google Scholar] [CrossRef] [PubMed]
  148. Sehested, L.T.; Pedersen, P. Prognosis and Risk Factors for intrauterine Growth Retardation. Dan. Med. J. 2014, 61, A4826. [Google Scholar]
  149. Ehrenberg, H.M.; Mercer, B.M.; Catalano, P.M. The Influence of Obesity and Diabetes on the Prevalence of Macrosomia. Am. J. Obstet. Gynecol. 2004, 191, 964–968. [Google Scholar] [CrossRef]
  150. Dai, R.X.; He, X.J.; Hu, C.L. Maternal Pre-Pregnancy Obesity and the Risk of Macrosomia: A Meta-Analysis. Arch. Gynecol. Obstet. 2018, 297, 139–145. [Google Scholar] [CrossRef]
  151. Goldstein, R.F.; Abell, S.K.; Ranasinha, S.; Misso, M.; Boyle, J.A.; Black, M.H.; Li, N.; Hu, G.; Corrado, F.; Rode, L.; et al. Association of Gestational Weight Gain with Maternal and Infant Outcomes: A Systematic Review and Meta-Analysis. J. Am. Med. Assoc. 2017, 317, 2207–2225. [Google Scholar] [CrossRef]
  152. Ahlsson, F.; Diderholm, B.; Jonsson, B.; Nordén-Lindberg, S.; Olsson, R.; Ewald, U.; Forslund, A.; Stridsberg, M.; Gustafsson, J. Insulin Resistance, a Link between Maternal Overweight and Fetal Macrosomia in Nondiabetic Pregnancies. Horm. Res. Paediatr. 2010, 74, 267–274. [Google Scholar] [CrossRef] [PubMed]
  153. Emerson, R.W.; Adams, C.; Nishino, T.; Hazlett, H.C.; Wolff, J.J.; Zwaigenbaum, L.; Constantino, J.N.; Shen, M.D.; Swanson, M.R.; Elison, J.T.; et al. Functional Neuroimaging of High-Risk 6-Month-Old Infants Predicts a Diagnosis of Autism at 24 Months of Age. Sci. Transl. Med. 2017, 9, eaag2882. [Google Scholar] [CrossRef] [PubMed]
  154. Conti, E.; Calderoni, S.; Marchi, V.; Muratori, F.; Cioni, G.; Guzzetta, A. Network Over-Connectivity Differentiates Autism Spectrum Disorder from Other Developmental Disorders in Toddlers: A Diffusion MRI Study. Hum. Brain Mapp. 2017, 38, 2333–2344. [Google Scholar] [CrossRef]
  155. Sun, H.; Li, Q.; Xiao, R.; Zhang, Z.; Yang, X.; Yang, J.; Jin, B.; Wen, J.; Wu, Y.; Yang, H.; et al. A Structural MRI Study of Global Developmental Delay in Infants (<2 Years Old). Front. Neurol. 2022, 13, 952405. [Google Scholar] [CrossRef]
  156. Soldateli, B.; Silveira, R.C.; Procianoy, R.S.; Belfort, M.; Caye, A.; Leffa, D.; Franz, A.P.; Barros, F.C.; Santos, I.S.; Matijasevich, A.; et al. Association between Preterm Infant Size at 1 Year and ADHD Later in Life: Data from 1993 and 2004 Pelotas Birth Cohorts. Eur. Child. Adolesc. Psychiatry 2023, 32, 1589–1597. [Google Scholar] [CrossRef]
  157. Wolff, J.J.; Swanson, M.R.; Elison, J.T.; Gerig, G.; Pruett, J.R.; Styner, M.A.; Vachet, C.; Botteron, K.N.; Dager, S.R.; Estes, A.M.; et al. Neural Circuitry at Age 6 Months Associated with Later Repetitive Behavior and Sensory Responsiveness in Autism. Mol. Autism 2017, 8, 8. [Google Scholar] [CrossRef]
  158. Luo, C.; Ye, W.-R.; Shi, W.; Yin, P.; Chen, C.; He, Y.-B.; Chen, M.-F.; Zu, X.-B.; Cai, Y. Perfect Match: MTOR Inhibitors and Tuberous Sclerosis Complex. Orphanet J. Rare Dis. 2022, 17, 106. [Google Scholar] [CrossRef]
  159. Chen, G.; Yu, B.; Tan, S.; Tan, J.; Jia, X.; Zhang, Q.; Zhang, X.; Jiang, Q.; Hua, Y.; Han, Y.; et al. GIGYF1 Disruption Associates with Autism and Impaired IGF-1R Signaling. J. Clin. Investig. 2022, 132, e159806. [Google Scholar] [CrossRef]
  160. Xing, X.; Zhang, J.; Wu, K.; Cao, B.; Li, X.; Jiang, F.; Hu, Z.; Xia, K.; Li, J.-D. Suppression of Akt-MTOR Pathway Rescued the Social Behavior in Cntnap2-Deficient Mice. Sci. Rep. 2019, 9, 3041. [Google Scholar] [CrossRef]
  161. Bozdagi, O.; Tavassoli, T.; Buxbaum, J.D. Insulin-like Growth Factor-1 Rescues Synaptic and Motor Deficits in a Mouse Model of Autism and Developmental Delay. Mol. Autism 2013, 4, 9. [Google Scholar] [CrossRef]
  162. Levy, J.A.; LaFlamme, C.W.; Tsaprailis, G.; Crynen, G.; Page, D.T. Dyrk1a Mutations Cause Undergrowth of Cortical Pyramidal Neurons via Dysregulated Growth Factor Signaling. Biol. Psychiatry 2021, 90, 295–306. [Google Scholar] [CrossRef] [PubMed]
  163. Shen, J.; Liu, L.; Yang, Y.; Zhou, M.; Xu, S.; Zhang, W.; Zhang, C. Insulin-Like Growth Factor 1 Has the Potential to Be Used as a Diagnostic Tool and Treatment Target for Autism Spectrum Disorders. Cureus 2024, 16, e65393. [Google Scholar] [CrossRef]
  164. Dönmez, B.; Erbakan, K.; Erbaş, O. The Role of Insulin-like Growth Factor on Autism Spectrum Disorder. J. Exp. Basic Med. Sci. 2021, 2, 430–435. [Google Scholar] [CrossRef]
  165. Dyer, A.H.; Vahdatpour, C.; Sanfeliu, A.; Tropea, D. The Role of Insulin-Like Growth Factor 1 (IGF-1) in Brain Development, Maturation and Neuroplasticity. Neuroscience 2016, 325, 89–99. [Google Scholar] [CrossRef] [PubMed]
  166. Welsh, J.P.; Yuen, G.; Placantonakis, D.G.; Vu, T.; Haiss, F.; O’Hearn, E.; Molliver, M.E.; Aicher, S.A. Why Do Purkinje Cells Die so Easily after Global Brain Ischemia? Aldolase C, EAAT4, and the Cerebellar Contribution to Posthypoxic Myoclonus. Adv. Neurol. 2002, 89, 331–359. [Google Scholar] [PubMed]
  167. Li, Z.; Xiao, G.-Y.; He, C.-Y.; Liu, X.; Fan, X.; Zhao, Y.; Wang, N.-R. Serum Levels of Insulin-like Growth Factor-1 and Insulin-like Growth Factor Binding Protein-3 in Children with Autism Spectrum Disorder. Chin. J. Contemp. Pediatr. 2022, 24, 186–191. [Google Scholar] [CrossRef]
  168. Anlar, B.; Oktem, F.; Bakkaloglu, B.; Haliloglu, M.; Oguz, H.; Unal, F.; Pehlivanturk, B.; Gokler, B.; Ozbesler, C.; Yordam, N. Urinary Epidermal and Insulin-Like Growth Factor Excretion in Autistic Children. Neuropediatrics 2007, 38, 151–153. [Google Scholar] [CrossRef]
  169. Mills, J.L.; Hediger, M.L.; Molloy, C.A.; Chrousos, G.P.; Manning-Courtney, P.; Yu, K.F.; Brasington, M.; England, L.J. Elevated Levels of Growth-Related Hormones in Autism and Autism Spectrum Disorder. Clin. Endocrinol. 2007, 67, 230–237. [Google Scholar] [CrossRef]
  170. Simsek, F.; Isık, Ü.; Aktepe, E.; Kılıc, F.; Sirin, F.B.; Bozkurt, M. Comparison of Serum VEGF, IGF-1, and HIF-1α Levels in Children with Autism Spectrum Disorder and Healthy Controls. J. Autism Dev. Disord. 2021, 51, 3564–3574. [Google Scholar] [CrossRef]
  171. Abedini, M.; Mashayekhi, F.; Salehi, Z. Analysis of Insulin-like Growth Factor-1 Serum Levels and Promoter (Rs12579108) Polymorphism in the Children with Autism Spectrum Disorders. J. Clin. Neurosci. 2022, 99, 289–293. [Google Scholar] [CrossRef]
  172. Courchesne, E.; Campbell, K.; Solso, S. Brain Growth across the Life Span in Autism: Age-Specific Changes in Anatomical Pathology. Brain Res. 2011, 1380, 138–145. [Google Scholar] [CrossRef] [PubMed]
  173. De Jong, M.; Cranendonk, A.; Twisk, J.W.R.; Van Weissenbruch, M.M. IGF-I and Relation to Growth in Infancy and Early Childhood in Very-Low-Birth-Weight Infants and Term Born Infants. PLoS ONE 2017, 12, e0171650. [Google Scholar] [CrossRef]
  174. Herbert, M.R.; Ziegler, D.A.; Makris, N.; Filipek, P.A.; Kemper, T.L.; Normandin, J.J.; Sanders, H.A.; Kennedy, D.N.; Caviness, V.S. Localization of White Matter Volume Increase in Autism and Developmental Language Disorder. Ann. Neurol. 2004, 55, 530–540. [Google Scholar] [CrossRef]
  175. Villar, J.; Ismail, L.C.; Victora, C.G.; Ohuma, E.O.; Bertino, E.; Altman, D.G.; Lambert, A.; Papageorghiou, A.T.; Carvalho, M.; Jaff, Y.A.; et al. International Standards for Newborn Weight, Length, and Head Circumference by Gestational Age and Sex: The Newborn Cross-Sectional Study of the INTERGROWTH-21 St Project. Lancet 2014, 384, 857–868. [Google Scholar] [CrossRef] [PubMed]
  176. Wilkin, T.J.; Murphy, M.J. The Gender Insulin Hypothesis: Why Girls Are Born Lighter than Boys, and the Implications for Insulin Resistance. Int. J. Obes. 2006, 30, 1056–1061. [Google Scholar] [CrossRef]
  177. Durston, S.; Hulshoff Pol, H.E.; Casey, B.J.; Giedd, J.N.; Buitelaar, J.K.; Van Engeland, H. Anatomical MRI of the Developing Human Brain: What Have We Learned? J. Am. Acad. Child. Adolesc. Psychiatry 2001, 40, 1012–1020. [Google Scholar] [CrossRef] [PubMed]
  178. Paus, T. Sex Differences in the Human Brain: A Developmental Perspective. Prog. Brain Res. 2010, 186, 13–28. [Google Scholar] [CrossRef]
  179. Bethlehem, R.A.I.; Seidlitz, J.; White, S.R.; Vogel, J.W.; Anderson, K.M.; Adamson, C.; Adler, S.; Alexopoulos, G.S.; Anagnostou, E.; Areces-Gonzalez, A.; et al. Brain Charts for the Human Lifespan. Nature 2022, 604, 525–533. [Google Scholar] [CrossRef]
  180. Geary, M.P.P.; Pringle, P.J.; Rodeck, C.H.; Kingdom, J.C.P.; Hindmarsh, P.C. Sexual Dimorphism in the Growth Hormone and Insulin-like Growth Factor Axis at Birth. J. Clin. Endocrinol. Metabol. 2003, 88, 3708–3714. [Google Scholar] [CrossRef]
  181. Yüksel, B.; Özbek, M.N.; Mungan, N.Ö.; Darendeliler, F.; Budan, B.; Bideci, A.; Çetinkaya, E.; Berberoǧlu, M.; Evliyaoǧlu, O.; Yeflilkaya, E.; et al. Serum IGF-1 and IGFBP-3 Levels in Healthy Children between 0 and 6 Years of Age. J. Clin. Res. Pediatr. Endocrinol. 2011, 3, 84–88. [Google Scholar] [CrossRef]
  182. Chellakooty, M.; Juul, A.; Boisen, K.A.; Damgaard, I.N.; Kai, C.M.; Schmidt, I.M.; Petersen, J.H.; Skakkebæk, N.E.; Main, K.M. A Prospective Study of Serum Insulin-like Growth Factor I (IGF-I) and IGF-Binding Protein-3 in 942 Healthy Infants: Associations with Birth Weight, Gender, Growth Velocity, and Breastfeeding. J. Clin. Endocrinol. Metabol. 2006, 91, 820–826. [Google Scholar] [CrossRef] [PubMed]
  183. Bunn, R.C.; King, W.D.; Winkler, M.K.; Fowlkes, J.L. Early Developmental Changes in IGF-I, IGF-II, IGF Binding Protein-1, and IGF Binding Protein-3 Concentration in the Cerebrospinal Fluid of Children. Pediatr. Res. 2005, 58, 89–93. [Google Scholar] [CrossRef] [PubMed]
  184. Shields, B.M.; Knight, B.; Hopper, H.; Hill, A.; Powell, R.J.; Hattersley, A.T.; Clark, P.M. Measurement of Cord Insulin and Insulin-Related Peptides Suggests That Girls Are More Insulin Resistant than Boys at Birth. Diabetes Care 2007, 30, 2661–2666. [Google Scholar] [CrossRef]
  185. Zur, R.L.; Kingdom, J.C.; Parks, W.T.; Hobson, S.R. The Placental Basis of Fetal Growth Restriction. Obstetr Gynecol. Clin. 2020, 47, 81–98. [Google Scholar] [CrossRef] [PubMed]
  186. Surico, D.; Bordino, V.; Cantaluppi, V.; Mary, D.; Gentilli, S.; Oldani, A.; Farruggio, S.; Melluzza, C.; Raina, G.; Grossini, E. Preeclampsia and Intrauterine Growth Restriction: Role of Human Umbilical Cord Mesenchymal Stem Cells-Trophoblast Crosstalk. PLoS ONE 2019, 14, e0218437. [Google Scholar] [CrossRef]
  187. Steinthorsdottir, V.; McGinnis, R.; Williams, N.O.; Stefansdottir, L.; Thorleifsson, G.; Shooter, S.; Fadista, J.; Sigurdsson, J.K.; Auro, K.M.; Berezina, G.; et al. Genetic Predisposition to Hypertension Is Associated with Preeclampsia in European and Central Asian Women. Nat. Commun. 2020, 11, 5976. [Google Scholar] [CrossRef]
  188. Depastas, C.; Kalaitzaki, A. The Epidemiology of Autism Spectrum Disorder and Factors Contributing to the Increase in Its Prevalence. Arch. Hellen Med. 2022, 39, 308–312. [Google Scholar]
  189. Wang, W.; Xie, X.; Yuan, T.; Wang, Y.; Zhao, F.; Zhou, Z.; Zhang, H. Epidemiological Trends of Maternal Hypertensive Disorders of Pregnancy at the Global, Regional, and National Levels: A Population-based Study. BMC Preg. Childbirth 2021, 21, 364. [Google Scholar] [CrossRef]
  190. Horgan, R.; Monteith, C.; McSweeney, L.; Ritchie, R.; Dicker, P.; EL-Khuffash, A.; Malone, F.D.; Kent, E. The Emergence of a Change in the Prevalence of Preeclampsia in a Tertiary Maternity Unit (2004–2016). J. Matern. Fet Neonat. Med. 2022, 35, 3129–3134. [Google Scholar] [CrossRef]
  191. Cameron, N.A.; Everitt, I.; Seegmiller, L.E.; Yee, L.M.; Grobman, W.A.; Khan, S.S. Trends in the Incidence of New-Onset Hypertensive Disorders of Pregnancy Among Rural and Urban Areas in the United States, 2007 to 2019. J. Am. Heart Assoc. 2022, 11, e023791. [Google Scholar] [CrossRef]
  192. Venkatesh, K.K.; Harrington, K.; Cameron, N.A.; Petito, L.C.; Powe, C.E.; Landon, M.B.; Grobman, W.A.; Khan, S.S. Trends in Gestational Diabetes Mellitus among Nulliparous Pregnant Individuals with Singleton Live Births in the United States between 2011 to 2019: An Age-Period-Cohort Analysis. Am. J. Obstet. Gynecol. MFM 2023, 5, 100785. [Google Scholar] [CrossRef]
  193. Wang, M.C.; Freaney, P.M.; Perak, A.M.; Greenland, P.; Lloyd-Jones, D.M.; Grobman, W.A.; Khan, S.S. Trends in Prepregnancy Obesity and Association with Adverse Pregnancy Outcomes in the United States, 2013 to 2018. J. Am. Heart Assoc. 2021, 10, e020717. [Google Scholar] [CrossRef] [PubMed]
  194. Fisher, S.C.; Kim, S.Y.; Sharma, A.J.; Rochat, R.; Morrow, B. Is Obesity Still Increasing among Pregnant Women? Prepregnancy Obesity Trends in 20 States, 2003–2009. Prev. Med. 2013, 56, 372–378. [Google Scholar] [CrossRef]
  195. Baraban, E.; McCoy, L.; Simon, P. Increasing Prevalence of Gestational Diabetes and Pregnancy-Related Hypertension in Los Angeles County, California, 1991–2003. Prev. Chronic Dis. 2008, 5, 1–9. Available online: http://www.cdc.gov/pcd/issues/2008/jul/07_0138.htm (accessed on 3 April 2025).
  196. Vishram, J.K.K.; Borglykke, A.; Andreasen, A.H.; Jeppesen, J.; Ibsen, H.; Jørgensen, T.; Palmieri, L.; Giampaoli, S.; Donfrancesco, C.; Kee, F.; et al. Impact of Age and Gender on the Prevalence and Prognostic Importance of the Metabolic Syndrome and Its Components in Europeans. the MORGAM Prospective Cohort Project. PLoS ONE 2014, 9, e107294. [Google Scholar] [CrossRef]
  197. Hildrum, B.; Mykletun, A.; Hole, T.; Midthjell, K.; Dahl, A.A. Age-Specific Prevalence of the Metabolic Syndrome Defined by the International Diabetes Federation and the National Cholesterol Education Program: The Norwegian HUNT 2 Study. BMC Publ. Health 2007, 7, 220. [Google Scholar] [CrossRef] [PubMed]
  198. Li, G.; Wei, T.; Ni, W.; Zhang, A.; Zhang, J.; Xing, Y.; Xing, Q. Incidence and Risk Factors of Gestational Diabetes Mellitus: A Prospective Cohort Study in Qingdao, China. Front. Endocrinol. 2020, 11, 636. [Google Scholar] [CrossRef]
  199. Vahratian, A. Prevalence of Overweight and Obesity among Women of Childbearing Age: Results from the 2002 National Survey of Family Growth. Matern. Child. Health J. 2009, 13, 268–273. [Google Scholar] [CrossRef]
  200. Oztan, O.; Garner, J.P.; Constantino, J.N.; Parker, K.J. Neonatal CSF Vasopressin Concentration Predicts Later Medical Record Diagnoses of Autism Spectrum Disorder. Proc. Natl. Acad. Sci. USA 2020, 117, 10609–10613. [Google Scholar] [CrossRef]
Figure 1. Cell types with highest level of IGF-1 receptor expression. Source: Human Protein Atlas (www.proteinatlas.org accessed on 29 April 2025) [112].
Figure 1. Cell types with highest level of IGF-1 receptor expression. Source: Human Protein Atlas (www.proteinatlas.org accessed on 29 April 2025) [112].
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Figure 2. Schematic diagram of age-dependent IGF-1 tone during and following gestational conditions with counteracting effect on fetal growth.
Figure 2. Schematic diagram of age-dependent IGF-1 tone during and following gestational conditions with counteracting effect on fetal growth.
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Table 1. Proposed phases of perinatal dysregulation of IGF-1 levels in ASD.
Table 1. Proposed phases of perinatal dysregulation of IGF-1 levels in ASD.
Time PeriodHormonal DysregulationEffect
Intrauterine developmentInsulin resistance and simultaneous growth restriction Close to normal birth weight
Early postnatal monthsSeparation from placental unit, release from growth restriction with prevailing insulin-resistanceRestoration of insulin secretion with elevated IGF-1 levels
First year Insulin-driven IGF-1 overproductionIGF-1-mediated overgrowth and accelerated neural development
From 12–15 months to ca. 4–5 yearsMaturation of GHRH-GH-IGF-1 axis inhibits IGF-1 productionArrest of growth and brain maturation
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Visegrády, A. The Possible Role of Postnatal Biphasic Dysregulation of IGF-1 Tone in the Etiology of Idiopathic Autism Spectrum Disorder. Int. J. Mol. Sci. 2025, 26, 4483. https://doi.org/10.3390/ijms26104483

AMA Style

Visegrády A. The Possible Role of Postnatal Biphasic Dysregulation of IGF-1 Tone in the Etiology of Idiopathic Autism Spectrum Disorder. International Journal of Molecular Sciences. 2025; 26(10):4483. https://doi.org/10.3390/ijms26104483

Chicago/Turabian Style

Visegrády, András. 2025. "The Possible Role of Postnatal Biphasic Dysregulation of IGF-1 Tone in the Etiology of Idiopathic Autism Spectrum Disorder" International Journal of Molecular Sciences 26, no. 10: 4483. https://doi.org/10.3390/ijms26104483

APA Style

Visegrády, A. (2025). The Possible Role of Postnatal Biphasic Dysregulation of IGF-1 Tone in the Etiology of Idiopathic Autism Spectrum Disorder. International Journal of Molecular Sciences, 26(10), 4483. https://doi.org/10.3390/ijms26104483

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