Showing posts with label Autism Biomarkers. Show all posts
Showing posts with label Autism Biomarkers. Show all posts

Wednesday 29 June 2022

Bumetanide - Biomarkers from Shanghai for Autism responders


“three cytokine levels, namely the IFN-γ, MIG and IFN-α2  … These cytokine levels at the baseline could improve the prediction of the bumetanide responders”

“… cytokines had a potential to construct a blood signature for predicting and monitoring the bumetanide treatment in young children with ASD.”

“a significant part of the clinical heterogeneity in the treatment effect of bumetanide for ASD is associated with the differences in the immune system of patients”


Autism is a very heterogeneous family of conditions and this is a big part of the reason why all clinical trials to date have failed.  Ideally, there would be a diagnostic test to identify which person will respond to which therapy.  Then you can have a successful clinical trial, because you are only including people likely to respond.

Researchers from China have just published their results that suggest that a blood test measuring three inflammatory markers can predict who will respond to bumetanide.  This is good news and where it is coming from is also very notable.

Autism research has been very fragmented, some of it is very sophisticated and insightful but much is very amateur and some is quite trivial.  There is usually a real lack of common sense among these people and no sense of urgency whatsoever.

China is a very organised country; plans are made and then they are implemented.  Forget political correctness.

This kind of approach is what is required to move along with autism treatment.

In addition, there is also another new study from China, this time on the microbiota in autism that compared those with and without GI problems (it found it is equally disturbed in both groups). Hopefully, that Chinese group will do the next common sense step and compare the microbiota of autistic people with and without restrictive diets. To what extent to people give themselves a microbiota problem through poor diet.


Disentangling the relationship of gut microbiota, functional gastrointestinal disorders and autism: a case–control study on prepubertal Chinese boys

The altered gastrointestinal microbiota composition in ASD appeared to be independent of comorbid functional gastrointestinal disorder


The bumetanide researchers are from Fudan University in Shanghai, one of the 3 ultra-selective Chinese Universities alongside Tsinghua University and Peking University in Beijing.

The paper, not surprisingly, may look complicated, but there are a great deal of interesting things in it.

In their words:-

An immuno-behavioural covariation was identified between symptom improvements in the Childhood Autism Rating Scale (CARS) and the cytokine changes among interferon (IFN)-γ, monokine induced by gamma interferon and IFN-α2. Using this covariation, three groups with distinct response patterns to bumetanide were detected

The three groups were: best responders, least responders and medium responders.

It should be noted that the dosage used in their trials was 0.5mg of bumetanide twice a day.

Chinese children tend to be smaller than Western children and this might help explain why the results were more positive than in Servier’s failed phase 3 clinical trial in Europe. I also imagine the Chinese children were more severely autistic than the European group.

The dosage used is selected to minimize the diuresis rather than to maximize the impact on the autism. This is understandable, but I think it is a mistake.


The immuno-behavioural covariation associated with the treatment response to bumetanide in young children with autism spectrum disorder 

Bumetanide, a drug being studied in autism spectrum disorder (ASD) may act to restore gamma-aminobutyric acid (GABA) function, which may be modulated by the immune system. However, the interaction between bumetanide and the immune system remains unclear. Seventy-nine children with ASD were analysed from a longitudinal sample for a 3-month treatment of bumetanide. The covariation between symptom improvements and cytokine changes was calculated and validated by sparse canonical correlation analysis. Response patterns to bumetanide were revealed by clustering analysis. Five classifiers were used to test whether including the baseline information of cytokines could improve the prediction of the response patterns using an independent test sample. An immuno-behavioural covariation was identified between symptom improvements in the Childhood Autism Rating Scale (CARS) and the cytokine changes among interferon (IFN)-γ, monokine induced by gamma interferon and IFN-α2. Using this covariation, three groups with distinct response patterns to bumetanide were detected, including the best (21.5%, n = 17; Hedge’s g of improvement in CARS = 2.16), the least (22.8%, n = 18; g = 1.02) and the medium (55.7%, n = 44; g = 1.42) responding groups. Including the cytokine levels significantly improved the prediction of the best responding group before treatment (the best area under the curve, AUC = 0.832) compared with the model without the cytokine levels (95% confidence interval of the improvement in AUC was [0.287, 0.319]). Cytokine measurements can help in identifying possible responders to bumetanide in ASD children, suggesting that immune responses may interact with the mechanism of action of bumetanide to enhance the GABA function in ASD.


The use of bumetanide as a potential drug to improve symptoms in ASD is based on a hypothesised pathoetiology of ASD, namely the delayed developmental switch of the gamma-aminobutyric acid (GABA) functioning from excitatory to inhibitory [10,11,12]. In the valproate and fragile X rodent models of autism, this GABA-switch can be facilitated by the reduction of intracellular chloride concentration, which is mediated by a sequential expression of the main chloride transporters, such as the potassium (K)-Cl co-transporters 2 (KCC2) and the importer Na-K-Cl cotransporter 1 (NKCC1) [12]. Therefore, bumetanide as an NKCC1 inhibitor has been tested for its ability to restore GABA function in ASD [5,6,71314]. However, these transporters can also be influenced by other molecules, such as cytokines, which are a number of small cell-signalling proteins closely interacting with each other to modulate the immune reactions. The cytokines have been implicated not only in brain development [15], but also in GABAergic transmission [16,17,18]. It has been reported that the interferon (IFN)-γ can decrease the levels of NKCC1 and the α-subunit of Na+-K+-ATPase, contributing to the restore of inhibitory GABA function [16]. In mice subjected to maternal deprivation, the interleukin (IL)-1 has also been found to reduce the expression of KCC2, delaying the developmental switch of the GABA function and thereby possibly contributing to the pathophysiology of developmental disorders such as ASD [1718]. Therefore, a question naturally arises that whether the treatment effect of bumetanide for ASD can be affected by the immune responses in the patients.

Indeed, compared with healthy controls, changes of the cytokine levels have already been reported in patients with ASD [19,20,21,22]. Recent meta-analyses showed that the levels of anti-inflammatory cytokines IL-10 and IL-1 receptor antagonist (Ra) were decreased [20], while proinflammatory cytokines IL-1β, IL-6 and anti-inflammatory cytokines IL-4, IL-13 were elevated in blood of patients with ASD [21]. The levels of IFN-γ, IL-6, tumour necrosis factor (TNF)-α, granulocyte-macrophage colony-stimulating factor (GM-CSF) and IL-8 were observed to be elevated [22] in postmortem brain tissues of ASD patients, and increased level of IFN-γ, monocyte chemotactic protein (MCP)-1, IL-8, leukaemia inhibitory factor (LIF) and interferon-gamma inducible protein (IP)-10 were found in another study [23]. These widely spread changes suggest that the cytokine signalling in ASD may be better characterised by multivariate patterns of cytokines. In literatures, many associations had been reported between the levels of cytokines (e.g., MCP-1, IL-1β, IL-4, IL-6, etc.) and both core symptoms and adaptive functions in children with ASD [24,25,26]. Therefore, it has been suggested that cytokines may be used as biomarkers to identify different subsets within ASD. In each of these subsets the patients with ASD may share a commonly immune-related pathoetiology and therefore may have similar profiles of response to treatment [27].

Based on these previous findings, we analysed data acquired through the Shanghai Xinhua ASD registry, China, that began in 2016 to test the hypothesis that the immune activity of patients might help to identify the best responders to bumetanide in ASD.


Between May 1st, 2018, to April 30th, 2019, a total of 90 ASD children, aged 3–10 years old, under a 3-month stable treatment of bumetanide without behavioural interventions and any concomitant psychoactive medications had both blood draws and behavioural assessments. Among these patients, 11 of them were further excluded due to the lack of the follow-up data at month 3. A group of 37 children, under 3-month stable treatment of placebo without behavioural interventions and any concomitant psychoactive medications had both blood draws and behavioural assessments. Therefore, the current analysis used a subsample of 116 young children with ASD, whose blood samples were available both before and after the treatment. The blood samples were sent in three batches (Discovery Set: n = 37 on December 4, 2019; Validation Set: n = 42 on May 22, 2019; and Control Set: n = 37 on January 5, 2022) to measure the serum levels of 48 cytokines for the immune response (Table S1), and the clinical symptoms were assessed using CARS, ADOS and the Social Responsiveness Scale (SRS). 

In this study, we observed a significant improvement of clinical symptoms with bumetanide treatment in children with ASD, and such improvement was associated with a pattern of changes in three cytokine levels, namely the IFN-γ, MIG and IFN-α2 (r = 0.459 in the Discovery Set and r = 0.316 in the Validation Set). These cytokine levels at the baseline could improve the prediction of the bumetanide responders compared with using the behavioural assessments alone, and the best predictor achieved an AUC of 0.83 in the independent test data set (Table S8). The implications of these findings may be twofold: (1) a significant part of the clinical heterogeneity in the treatment effect of bumetanide for ASD is associated with the differences in the immune system of patients, and (2) the component score of cytokines had a potential to construct a blood signature for predicting and monitoring the bumetanide treatment in young children with ASD.

Following the protocols of previous studies [8], bumetanide treatment consisted of two 0.5 mg tablets per day for three months, given at 8:00 a.m. and 4:00 p.m. The tablet size is 8 mm diameter x 2 mm thickness, which is quite small. Each time, the patient took half of a tablet, which was not difficult for most of the patients. However, the careers were recommended to grind the half-tablet into powder and give the powder in water, if necessary. Possible side effects were closely monitored during the treatment. Blood parameters (serum potassium and uric acid) were monitored via laboratory tests (Table S2) and symptoms (thirst, diuresis, nausea, vomiting, diarrhoea, constipation, rash, palpitation, headache, dizziness, shortness of breath, and any other self-reported symptoms) were telephone interviewed (Table S3), and both of them were reported to the research team by telephone at 1 week and 1 month after the initiation of treatment and at the end of the treatment period. The cytokine levels of the children with gastrointestinal problems were compared with those without such problems (Table S4).

The supplemental table S4 shows that GI problems had no effect on cytokine levels.

Changes after the administration of bumetanide

Seventy-nine patients were treated with bumetanide for 3 months, and the CARS total score decreased after the treatment (effect size Cohen’s d = 1.26, t78 = 11.21, p < 0.001). The treatment effect showed no difference between the Discovery Set and the Validation Set (ΔCARS_total: mean(±SD): 1.54 (±1.40) vs. 1.90 (±1.34)). Consistent to the previous studies of the low-dose bumetanide for ASD, the side effects were rarely reported (Tables S2 and S3). No significant difference in the cytokine levels between the children with and without the gastrointestinal problems at the baseline (Table S4). A number of cytokine levels were changed significantly after the treatment of bumetanide, but none of them was changed significantly after the treatment of placebo (Table S6). No significant pairwise association could be identified in the Discovery Set, the Validation Set and the Control Set among four groups of variables, including the baseline CARS total score, the baseline cytokine levels, the change of CARS total score, and the changes of cytokine levels (Fig. S2).


In this study, we observed a significant improvement of clinical symptoms with bumetanide treatment in children with ASD, and such improvement was associated with a pattern of changes in three cytokine levels, namely the IFN-γ, MIG and IFN-α2 (r = 0.459 in the Discovery Set and r = 0.316 in the Validation Set). These cytokine levels at the baseline could improve the prediction of the bumetanide responders compared with using the behavioural assessments alone, and the best predictor achieved an AUC of 0.83 in the independent test data set (Table S8). The implications of these findings may be twofold: (1) a significant part of the clinical heterogeneity in the treatment effect of bumetanide for ASD is associated with the differences in the immune system of patients, and (2) the component score of cytokines had a potential to construct a blood signature for predicting and monitoring the bumetanide treatment in young children with ASD.

Accumulating evidences support that IFN-γ can inhibit chloride secretion [38] and down-regulate both the NKCC1 expression [1638] and the Na+-K+-ATPase expression [16], which had been implicated in the GABAergic dysfunction in ASD [1039].


The cytokine-symptom association was identified in the changes after the treatment of bumetanide but not before the treatment, suggesting that bumetanide might interact with the cytokines and the changes of which contributed to the treatment effect of bumetanide. Animal studies showed a rapid brain efflux of bumetanide, but a number of clinical trials have shown a significant treatment effect for neuropsychiatric disorders, including ASD, epilepsy and depression [4142]. These findings may suggest the possible systemic effects of bumetanide as a neuromodulator for these neuropsychiatric disorders. Considering its molecular structure, bumetanide has been recently identified by an in vitro screen of small molecules that can act as an anti-proinflammatory drug via interleukin inhibition [43]. This anti-proinflammatory activity of bumetanide might alter the blood levels of cytokines outside the brain-blood-barrier (BBB).

Our findings may suggest that the identified canonical score of cytokines had a potential to construct a blood signature for predicting and monitoring the bumetanide treatment in young children with ASD. Accurately identifying patients who are likely to respond positively to bumetanide can facilitate the precision medicine for ASD. Our prediction model based on the cytokine levels before the treatment may provide a potentially new tool for the precision medicine of ASD. 


In summary, we identified an association between the changes of the cytokine levels and the improvements in symptoms after the bumetanide treatment in young children with ASD, and found that the treatment effect of bumetanide can be better characterised by an immuno-behavioural covariation. This finding may provide new clinically important evidence supporting the hypothesis that immune responses may interact with the mechanism of bumetanide to restore the GABAergic function in ASD. This finding may also have relevance for determining enriched samples of ASD children to participate in novel drug treatment studies of drugs with a similar mode of action to bumetanide, but with potentially greater efficacy and fewer side effects.



I think we can give the Shanghai researchers 10 out of 10 for their paper.

Monty, aged 18 with ASD, has been to Shanghai twice. It is a vast city, but well worth a visit. With the high speed train network it is now very easy to travel around China, quite different to when I visited as a teenager.

Hopefully the Chinese will continue in their pursuit of precision medicine for autism. They do not have much competition.

My perspective is a little different because I know that a bumetanide responder can cease to be a responder when affected by an inflammatory condition like allergy, which increases pro-inflammatory cytokines like IL-6. This suggests that some people with elevated cytokines are potential responders, you just have to use an anti-inflammatory therapy before you start bumetanide therapy. The inflammatory cytokines shift the balance between NKCC1 and KCC2 towards NKCC1 and so increasing intracellular chloride.  We also know that some people need a dose higher than 0.5mg twice a day to see a large benefit; I have been using 2mg once a day for several years.

The Chinese researchers have established biomarkers for who is likely now to respond to bumetanide. This certainly is a big step forward, if it can be replicated. This is not the same as identifying who could respond to bumetanide, if their current inflammatory condition was moderated. The levels of specific cytokines might indeed mark someone as both a current non-responder, but also as a potential future responder.

Autism is all about n=1, it is about the exceptions being more important than the average.

Unlike the Shanghai researchers, I do not really see Bumetanide as an anti-inflammatory therapy in my son’s Polypill, but I do have therapies included that are.

Understanding inflammation will be a key to treating autism using precision medicine.  That is less simple that it sounds. When it comes to preventing autism, inflammation in the mother is a key part of the equation. This also gets complicated, maternal antibodies damage the brain of the fetus, no genetic mutations were needed.

Wednesday 21 October 2015

Biomarkers in Autism

This post has been sitting unfinished for a while, so I decided to publish it before I forget all about it

The two papers discussed today really confirm much of what we have already established in this blog, but they are very useful as a recap and for those with limited time.

The first paper is extremely comprehensive and, if you go through it very slowly, really tells you much of what you need to know about the biology of autism.  It is some wonder that so few clinicians are aware of these findings.


Autism spectrum disorders (ASDs) are complex, heterogeneous disorders caused by an interaction between genetic vulnerability and environmental factors. In an effort to better target the underlying roots of ASD for diagnosis and treatment, efforts to identify reliable biomarkers in genetics, neuroimaging, gene expression, and measures of the body’s metabolism are growing. For this article, we review the published studies of potential biomarkers in autism and conclude that while there is increasing promise of finding biomarkers that can help us target treatment, there are none with enough evidence to support routine clinical use unless medical illness is suspected. Promising biomarkers include those for mitochondrial function, oxidative stress, and immune function. Genetic clusters are also suggesting the potential for useful biomarkers.

Here are the key parts; I do suggest you read the full text of the paper.

Metabolic Biomarkers

There are no autism-defining, metabolic biomarkers, but examining the biomarkers of pathways associated with ASD can point to potentially treatable metabolic abnormalities and provide a baseline that can be tracked over time. Each child may have different metabolic pathologies related to SNPs, nutrient deficiencies, and toxic exposures. Examples of metabolic disorders that can lead to an autistic-like presentation include phenylketonuria (PKU) (37), disorders of purine metabolism (38), biotinidase deficiency (39), cerebral folate deficiency (40), creatine deficiency (41), and excess propionic acid (which is produced by Clostridium) (42, 43).

A recent review assessed the research on physiological abnormalities associated with ASD (44). The authors identified four main mechanisms that have been increasingly studied during the past decade: immunologic/inflammation, oxidative stress, environmental toxicants, and mitochondrial abnormalities. In addition, there is accumulating research on the lipid, GI systems, microglial activation, and the microbiome, and how these can also contribute to generating biomarkers associated with ASD.

The brain is highly vulnerable to oxidative stress (51), particularly in children (52) during the early part of development (47). As environmental events and metabolic imbalances affect oxidative stress and methylation, they also can affect the expression of genes.

Several studies have detected altered levels of a large collection of substances in body-based fluids from ASD subjects compared to controls (e.g., serum, whole-blood, and CSF) (53). These findings encompass either of two main disease-provoking mechanisms: a CNS disorder that is being detected peripherally [e.g., serotonin and its metabolites, sulfate (54), low platelet levels of gamma-aminobutyric acid (GABA) (55), low oxytocin (which affects social affiliation) (56), and low vitamin D levels (57, 58)] or a systemic abnormality that has repercussions in the brain (59).

Oxidative stress markers

Oxidative stress can be detected by studying antioxidant status, antioxidant enzymes, lipid peroxidation, and protein/DNA oxidation, all of which have been found to be elevated in children with autism (Table (Table2).2). Different subgroups of children with ASD have different redox abnormalities, which may arise from various sources

Measurements of antioxidant status include measurement of glutathione, the primary antioxidant in the protection against oxidative stress, neuroinflammation, and mitochondrial damage (68, 69). Glutathione is instrumental in regulating detoxification pathways and modulates the production of precursors to advanced glycation end products (AGEs) (70). Measuring reduced glutathione, oxidized glutathione, or the ratio of reduced glutathione to oxidized glutathione helps determine the patient’s oxidation status. In many patients with ASD, the ratio of reduced glutathione to oxidized glutathione is decreased, indicating a poor oxidation status

The enzyme glutathione peroxidase has been used as a marker and is typically reduced. There are mixed results concerning the enzyme levels of superoxide dismutase (SOD) (72). Other markers for glutathione inadequacy include alpha hydroxybutyrate, pyroglutamate, and sulfate, which can be assessed in an organic acid test. Lipid peroxidation refers to the oxidative degradation of cell membranes. There is a significant correlation between the severity autism and urinary lipid peroxidation products (67), which are increased in patients with ASD

Plasma F2t-Isoprostanes (F2-IsoPs) are the most sensitive indicator of redox dysfunction and are considered by some to be the gold standard measure of oxidative stress (73). They are increased in patients with ASD and are even higher when accompanied by gastrointestinal dysfunction (73).

Decreased levels of major antioxidant serum proteins transferrin (iron-binding protein) and ceruloplasmin (copper binding protein) have been observed in patients with ASD. The levels of reduction in these proteins correlate with loss of previously acquired language (47) although there are mixed reviews of the significance of this (66).

Plasma 3-chlortyrosine (3CT), a measure of reactive nitrogen species and myeloperoxidase activity, is an established biomarker of chronic inflammatory response. Plasma 3CT levels reportedly increased with age for those with ASD and mitochondrial dysfunction but not for those with ASD without mitochondrial dysfunction (65).

3-Nitrotyrosine (3NT) is a plasma measure of chronic immune activation and is a biomarker of oxidative protein damage and neuron death. This measure correlates with several measures of cognitive function, development, and behavior for subjects with ASD and mitochondrial dysfunction but not for subjects with ASD without a mitochondrial dysfunction (65).

Mitochondrial dysfunction markers

Mitochondrial dysfunction is marked by impaired energy production. Some children with ASD are reported to have a spectrum of mitochondrial dysfunction of differing severity (44) (Table (Table3).3). Mitochondrial dysfunction, most likely an early event in neurodegeneration (76), is one of the more common dysfunctions found in autism (77) and is more common than in typical controls (78). There is no reliable biomarker to identify all cases of mitochondrial dysfunction (79). It is possible that up to 80% of the mitochondrial dysfunction in patients with both ASD and a mitochondrial disorder are acquired rather than inherited (44).

Mitochondrial dysfunction can be a downstream consequence of many proposed factors including dysreactive immunity and altered calcium (Ca2+) signaling (80), increased nitric oxide and peroxynitrite (68), propionyl CoA (81), malnutrition (82), vitamin B6 or iron deficiencies (83), toxic metals (83), elevated nitric acid (84, 85), oxidative stress (86), exposure to environmental toxicants, such as heavy metals (8789), chemicals (90), polychlorinated biphenyls (PCBs) (91), pesticides (92, 93), persistent organic pollutants (POPs) (94), and radiofrequency radiation (95). Other sources of mitochondrial distress include medications such as valproic acid (VPA), which inhibits oxidative phosphorylation (96) and neuroleptics (97, 98).

Markers of mitochondrial dysfunction include lactate, pyruvate and lactate-to-pyruvate ratio, carnitine (free and total), quantitative plasma amino acids, ubiquinone, ammonia, CD, AST, ALT, CO2 glucose, and creatine kinase (CK) (44). Many studies of ASD report elevations in lactate and pyruvate, others report a decrease in carnitine, while others report abnormal alanine in ASD patients (44) or elevations in aspartate aminotransferase and serum CK (99). Increases in lactate are not specific and may only occur during illness, after exercise or struggling during a blood draw (100).

Rossignol and Frye (44) recommend a mitochondrial function screening algorithm. This includes fasting morning labs of lactate, pyruvate, carnitine (free and total), acyl carnitine panel, quantitative plasma amino acids, ubiquinone, ammonia, CK, AST/ALT, CO2, and glucose (44). The interpretation of such a panel and the indications for specific treatments has not yet been established.


The methylation pathway provides methyl groups for many functions, including the methylation of genes, which can result in the epigenetic changes of turning genes on and off (Table (Table4).4). This transfer occurs when S-adenosylmethionine (SAM) donates a methyl group and is transformed to S-adenosylhomocysteine (SAH). SAH can be transferred to homocysteine, which can either be re-methylated to methionine or be transferred by the sulfuration pathway to cysteine to create glutathione. With increased oxidative stress, SAH might be diverted away from the methylation pathway to the sulfuration pathway in order to make more glutathione. This will result in less methionine and less methylation ability.

A marker of methylation dysfunction is decreased SAM/SAH ratio in patients with ASD. Fasting plasma methionine decreases since through SAM it is the main methyl donor. Fasting plasma cysteine, a sulfur containing amino acid is the rate-limiting step in the production of glutathione and is significantly decreased. Plasma sulfate is decreased, which may impair detoxification pathways. Homocysteine is generally increased, but the studies are mixed (66). Vitamin B12 and folate are required for the methylation pathway. The MTHFR genetic SNP is reported to heavily influence the methylation pathway (66).

Immune dysregulation

Cytokine evaluation

Chronic inflammation and microglia cell activation is present in autopsied brains of people with ASD (101, 102) (Table (Table5).5). Factors that increase the risk of activating brain microglia include traumatic brain injury (TBI) (103) reactive oxygen species (104) and a dysfunctional blood brain barrier (105). The blood brain barrier can be compromised by oxidative stress (106), acutely stressful situations (107), elevated homocysteine (108), diabetes (109), and hyperglycemia (110). Cytokines can pass through a permeable blood brain barrier and start this process (111). Hence, cytokines can serve as a marker of the immune dysregulation, which can further complicate ASD.

Autoimmunity and maternal antibodies

Autoimmune autistic disorder is proposed as a major subset of autism (118), and autoimmunity may play a role in the pathogenesis of language and social developmental abnormalities in a subset of children with these disorders (119). There are many autoantibodies found in the nervous system of children with ASD who have a high level of brain antibodies (120, 121). These can be measured as biomarkers in this subset of ASD patients. The anti ganglioside M1 antibodies (122), antineuronal antibodies (123), and serum anti-nuclear antibodies (123, 124) correlate with the severity of autism. Other autoantibodies postulated to play a pathological role in autism include: anti neuron-axon filament protein (anti-NAFP) and glial fibrillary acidic protein (anti-GFAP) (125), antibodies to brain endothelial cells and nuclei (119), antibodies against myelin basic protein (126, 127), and anti myelin associated glycoprotein, an index for autoimmunity in the brain (128). BDNF antibodies were found higher in ASD (129), and low BDNF levels may be involved in the pathophysiology of ASD (130).

Antibodies in patients with autism are found to cells in the caudate nucleus (131), cerebellum (132, 133), hypothalamus and thalamus (121), the cingulate gyrus (134), and to cerebral folate receptors (135). Children with cerebellar autoantibodies had lower adaptive and cognitive function as well as increased aberrant behaviors compared to children without these antibodies (132).

Mother’s immune status

Research studies indicate an association between viral or bacterial infections in expectant mothers and their ASD offspring (136, 137). Maternal antibodies cross the underdeveloped blood brain barrier of the fetus (138) leading to impaired fetal neurodevelopment and long-term neurodegeneration, neurobehavioral, and cognitive difficulties (139).


When the gut becomes inflamed, it breaks down and becomes permeable, sometimes referred to as dysbiosis. Dysbiosis is reported to be an upstream contributing factor to autoimmune conditions and inflammation. Markers under consideration include circulating antibodies against tight junction proteins, LPS, actomyosin (145) calprotectin (146), and lactoferrin (147). Dysbiosis was found in 25.6% of patients with ASD (148). It is proposed to have a direct effect on the brain as it is a hypothesized source of inflammation (149151) and autoimmunity (152, 153), possibly through molecular mimicry (154). Diet is one source of dysbiosis (155).

Amino acids and neuropeptides

Platelet hyperserotonemia is considered one of the most consistent neuromodulator findings in patients with ASD (Table (Table6).6). As for other neuropeptides, a recent review reported approximately 15 components that are altered in ASD compared to controls (53). Among them, interesting research has been done on glutamate, GABA, BDNF, and dopamine and noradrenaline systems. A recent study reported a positive correlation between severity of clinical symptoms and plasma GABA levels in patients with ASD, supporting the idea of a disrupted GABAergic system (156).


Fatty acid analysis

Abnormal fatty acid metabolism may play a role in the pathogenesis of ASD and may suggest some metabolic or dietary abnormalities in the regressive form of autism (42, 157). There is evidence of a relationship between changes in brain lipid profiles and the occurrence of ASD-like behaviors using a rodent model of autism (42). Hyperactivity in patients was inversely related to the fluidity of the erythrocyte membrane and membrane polyunsaturated fatty acid (PUFA) levels (158). Imbalances of membrane fatty acid composition and PUFA loss can affect ion channels and opiate, adrenergic, insulin receptors (159) and the modulation of (Na + K)-ATPase activity (160). Analysis of red blood cell membrane fatty acids is a very sensitive indicator of tissue status and may reflect the brain fatty acid composition (161).
Seventeen percent of children with ASD manifest biomarkers of abnormal mitochondrial fatty acid metabolism, the majority of which are not accounted for by genetic mechanisms (162). Patients with ASD had reduced percentages of highly unsaturated fatty acids (163) and an increase in ω6/ω3 ratio (158).

Biomedical Interventions

There are no published studies of interventions for ASD that use neuroimaging or genetic biomarkers in a prospective manner to guide treatment. Biomedical interventions based on body fluid/product biomarkers have been used in a small but growing numbers of well designed, published studies. Several recent reviews summarize these.

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If you have managed to digest all of that information, here is another very interesting paper.

The researchers are, as so often, from Johns Hopkins.  This time they propose an idea to simplify the understanding of the bewildering number of autism sub-types.

I have frequently commented in this blog that in many identified underlying dysfunctions, being hyper (too much) or hypo (two little) causes the same effect, i.e. autism.

They split autism into:-

·        hyper-active pro-growth signaling pathways (e.g. big heads)
·        hypo-active pro-growth signaling pathways  (e.g. small heads)

So the first question is whether the patient is type A or type B.

It is definitely a step forward in simplifying what is going on, so that one day a clinician, without being a Nobel Laureate, could treat autism without just using trial and error.  If the clinician had also read, and understood, the first paper, he/she really would be able to help the patient.

The genetic and phenotypic heterogeneity of autism spectrum disorders (ASD) presents a substantial challenge for diagnosis, classification, research, and treatment. Investigations into the underlying molecular etiology of ASD have often yielded mixed and at times opposing findings. Defining the molecular and biochemical underpinnings of heterogeneity in ASD is crucial to our understanding of the pathophysiological development of the disorder, and has the potential to assist in diagnosis and the rational design of clinical trials. In this review, we propose that genetically diverse forms of ASD may be usefully parsed into entities resulting from converse patterns of growth regulation at the molecular level, which lead to the correlates of general synaptic and neural overgrowth or undergrowth. Abnormal brain growth during development is a characteristic feature that has been observed both in children with autism and in mouse models of autism. We review evidence from syndromic and non-syndromic ASD to suggest that entities currently classified as autism may fundamentally differ by underlying pro- or anti-growth abnormalities in key biochemical pathways, giving rise to either excessive or reduced synaptic connectivity in affected brain regions. We posit that this classification strategy has the potential not only to aid research efforts, but also to ultimately facilitate early diagnosis and direct appropriate therapeutic interventions.