Tracking Progress
Kitzerow’s Autism and the Comorbidities Cascade Progress Tracker
Tracking the progression of the hypothesis through biological validation and eventual translation.
2023
2026
The scale summarizes overall progress. Individual nodes are graded separately in the tracker below.
Biological Coherence
What This Tracker Evaluates
The central question is whether independently observed findings increasingly align with the biological relationships predicted by the Cascade. Every node follows the same sequence so readers can distinguish the proposed mechanism, its prior scientific foundation, subsequent findings, and the work still required for validation.
Optional translation outlookHow Validated Nodes Could Eventually TranslateExplore the five potential levels from downstream trait management to upstream biological targets.
Translation Outlook
How Validated Nodes Could Eventually Translate
The five BioToggle® levels show how validated biological nodes could eventually support increasingly upstream measurement and intervention. They are a translational map, not evidence that every proposed node has already been validated or can currently be treated.
From Validation to Translation
Five Potential Translation Levels
BioToggle® maps how biological coherence could eventually translate from downstream trait management toward upstream, node-specific intervention. Progress depends first on validating which nodes are active, how they relate, and which predicted effects they produce. Because interacting variables differ, the relevant node or combination of nodes may also differ among individuals.
Treat the Individual Trait
This is how most treatment currently works. A downstream medical or physiological trait develops and that trait is treated directly.
- Sleep problems are treated as sleep problems.
- Seizures are treated as seizures.
- Gastrointestinal problems are treated individually.
- Mental health traits are treated through their downstream systems.
- Autonomic and metabolic traits are managed according to the affected system.
These interventions may improve the individual trait without changing the upstream mechanism that produced it.
Target a Downstream Pathway
Treatment can move one level upstream by targeting a pathway known to contribute to one or more downstream traits.
- Neurotransmitter signaling
- Excitation and inhibition balance
- mTOR activity
- Endocannabinoid signaling
- Immune and inflammatory pathways
- Oxidative stress and metabolic pathways
This may influence several downstream effects, but treatment is still occurring below the proposed mechanism responsible for shifting pathway activity.
Target the Shunt Producing the Pathway Change
The Cascade proposes that many downstream pathway abnormalities are not isolated failures. They reflect changes in how biological resources are allocated between competing pathways according to physiological demand.
- BH4 Shunt
- AAAH Shunt
- NOS Shunt
- AGMO Shunt
- Epigenetic redox-sensitive protein shunts
At this level, the treatment question changes from “How do we increase or decrease this pathway?” to “Why is biological activity being redirected in the first place?”
Restore Regulatory-System Balance
BioToggle® proposes that the shunts occur within a larger regulatory response involving the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains.
These systems interact continuously to maintain biological balance and restore set points after physiological demands change.
Treatment at this level would focus on why a set point was breached, what is preventing restoration, and how balance can be restored within and between interconnected regulatory systems.
Target the Upstream Genetic or Protein Driver
Genetic variation can alter protein function upstream of pathway and regulatory-system activity.
Treatment at this level moves closest to the beginning of the biological Cascade.
Gene, RNA, and protein-directed approaches could potentially modify an upstream biological driver before its effects propagate throughout the downstream cascade.
Why Move Upstream?
The farther upstream a shared mechanism sits, the more downstream effects it may influence. That is why identifying shared biology matters for understanding and eventually treating comorbidity clustering.
Comorbidity Clustering Changes the Treatment Question
Comorbidity clustering means multiple traits repeatedly occur together rather than appearing as completely unrelated conditions.
If five traits are produced by five unrelated mechanisms, they may require five different treatments. If those traits share one upstream mechanism, that shared mechanism creates an additional treatment target.
The goal is therefore not only to identify which comorbid conditions occur in autism. It is to determine which traits cluster together, what biology they share, where those pathways converge, and whether that shared mechanism can be targeted.
Model-level falsifiabilityWhat Would Falsify the Cascade?See the evidence that could disconfirm the Cascade's predictions.
The Cascade does not predict that every autistic individual will have the same biomarkers, the same most-upstream identifiable node, or the same cluster of autism and comorbid traits. Individual variation is expected in which biological systems are affected, when dysregulation occurs, how long it persists, and how biological resources are allocated throughout the Cascade.
The Cascade also includes feedback, reinforcing, inhibitory, and compensatory relationships. It does not require every biological interaction to operate in only one direction.
The model predicts that measurable dysregulation within the Cascade will correspond to specific downstream biological effects and distinguishable clusters of autism and comorbid traits. The Cascade would be challenged if those predicted biomarker patterns, trait classifications, or biological mechanisms repeatedly failed when tested in relevant populations and datasets.
Each node can be falsified in three primary ways:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by that node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For upstream nodes, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect is demonstrated to be biologically inaccurate.
Failure at one node may require revision of that node, its assigned traits, or its relationship to the rest of the Cascade. Whole-Cascade falsification would require repeated failure of the broader prediction that dysregulation within the Cascade produces biologically traceable and distinguishable trait patterns.
Current Progress
2023 → 2026Two Connected Validation Tracks
Why the Model Is Separated Into Two Cascades
The two Cascades share proposed upstream biology, but they follow that biology toward different predicted outcomes. Separating them makes it possible to evaluate whether each node is connected to the specific type of trait assigned to it without implying that autism traits and comorbid traits are interchangeable.
Tracks predicted effects on neural circuitry, neural connectivity, skill and behavior development, regression, and social-interaction or social-avoidance traits.
Tracks predicted medical and physiological effects, regulatory-system traits, comorbidity clustering, allostatic overload, and chronic progressive conditions.
The Cascades are separated for validation, not because they operate independently. A shared upstream node may contribute to both tracks, while downstream branches require different evidence, outcome measures, and eventual forms of translation.
Autism Traits Cascade
Mechanism: Regulatory system domain activation (immune system, metabolism, cellular repair, nervous system and genetic regulation. Either due to situational activation that becomes chronically impactful due to overload or genetic factors.
Mechanism: CSTL excitation/inhibition imbalance disrupts the formation and activity within circuits that produce movement, habit formation, and reward. Driving dysregulation in skill/behavior development. Excitotoxic synaptic damage impacts memories for how to produce skills and behaviors.
Mechanism: mTOR-mediated synaptic pruning dysregulation alters circuit refinement and connectivity patterns.
Mechanism: lipid reorganization alters endocannabinoid signaling, microglial activity, and social-regulatory pathway activity.
Chronic Allostasis
1. Proposed Mechanism
Within the model, genetic and epigenetic factors chronically activate the regulatory system domains (Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation) and disrupt the temporal system domains (Ultradian, Circadian, Circannual, Developmental, and Aging). The model proposes that this sustained allostatic state reallocates biological resources toward resolving the stress response and away from typical development and function, producing biochemically predictable downstream changes throughout the cascade. Persistent activation is further proposed to result in allostatic overload, contributing to the accumulation of compounding comorbid traits across regulatory domains.
2. Prior Converging Evidence
- Makris et al. (2022) . Autonomic Nervous System Dysregulation in Autism.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- UCSD Dr. Naviaux’s 3-Hit Metabolic Signaling Model (2025) . Proposes that autism emerges through the interaction of genetic, chronic, and situational stressors...
- Santamaría-García et al. (2025) . Allostatic Interoception and Neurodevelopment.
4. What Remains Untested
- Biomedical approaches commonly target inflammation, oxidative stress, mitochondrial dysfunction, immune dysregulation, metabolic dysfunction, and other contributors to chronic physiological stress.
- Current approaches typically target individual pathways rather than regulatory-system domains and their interactions.
- The model predicts that restoring balance within and between regulatory domains may be more important than continuously driving individual pathways higher or lower.
- Better understanding is needed regarding biological set-point restoration across interconnected systems.
- Increasing recognition of chronic physiological stress across autism research accompanied by genetic and epigenetic factors.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
BH4 Shunt
1. Proposed Mechanism
Mechanism
Within the model, chronic allostasis activates the BH4 Shunt, a proposed redox-sensitive shift in GCH1 regulation that reallocates BH4-dependent biological resources toward survival functions as a core component of the stress response system. The model proposes that this redistribution initiates predictable downstream changes in BH4-dependent pathways involved in autism traits and comorbid traits. As a central regulator of multiple interconnected biological systems, BH4 Shunt activation is proposed to generate cascading effects that produce autism traits, comorbid traits, and comorbidity clustering.
2. Prior Converging Evidence
- Frye (2010): BH4 Treatment in Autism. Investigated tetrahydrobiopterin (BH4) as a therapeutic intervention for autism based on its role as a critical cofactor in neurotransmitter synthesis and nitric oxide metabolism. Sixty-three percent of participants demonstrated clinical improvement following BH4 treatment. The study approached BH4 dysfunction as a deficiency state rather than a BH4 shunt mechanism. Within Kitzerow’s model, variability in treatment response may reflect underlying BH4 pathway redistribution rather than BH4 deficiency alone.
- Klaiman et al. (2013): BH4 Placebo-Controlled Trial in Autism. Found significant improvements in social awareness, autism mannerisms, hyperactivity, and inappropriate speech following BH4 treatment compared to placebo.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Filho et al. (2025): BH4 Systematic Review. Concluded that redox-regulated BH4 dysfunction impacts multiple interconnected biological systems that contribute to autism and associated comorbid traits.
4. What Remains Untested
Research Gap
- Existing interventions increase BH4 availability but do not directly target the proposed BH4 Shunt mechanism.
- The model predicts that increasing BH4 levels and modifying BH4 Shunt activation may not represent the same intervention target.
- Direct BH4 Shunt interventions have not yet been developed.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
AGMO Shunt
1. Proposed Mechanism
Mechanism
Within the model, the AGMO Shunt is a BH4-dependent lipid-regulation mechanism that activates following a regulatory system set-point breach. AGMO regulates the catabolism of alkylglycerol ether lipids. Ether lipids include plasmanyl phospholipids, plasmalogens, platelet-activating factor (PAF), and endocannabinoid-related lipids such as noladin ether. The model proposes that changes in AGMO pathway activity alter lipid signaling and regulatory function across biological systems, producing comorbid traits. The specific traits produced by AGMO pathway dysregulation appear to be involved in social interaction and social avoidance.
2. Prior Converging Evidence
- Dorninger et al. (2019): Ether Lipid Deficiency and Neurotransmitter Homeostasis . Using a Gnpat knockout mouse model of ether lipid deficiency, the authors demonstrated reduced neurotransmitter levels, altered neurotransmitter turnover, impaired vesicular monoamine transport, reduced neurotransmitter release, hyperactivity, and impaired social interaction.
- Smith et al. (2023): Ether Lipid and Glycerophospholipid Dysregulation in Autism . Identified reproducible alterations in ether lipid subclasses, glycerophospholipid domains, and long-chain polyunsaturated fatty acids including linoleic acid, arachidonic acid, and DHA in autistic individuals. This provides converging evidence that lipid pathways involved in membrane composition, ether lipid metabolism, and endocannabinoid precursor availability are altered in autism.
- Su et al. (2021): Endocannabinoid Signaling, Social Reward, and Microglial Regulation . Reviewed evidence demonstrating that the endocannabinoid system participates in the regulation of social reward behavior and has emerged as a potential therapeutic target in autism. The authors further note that microglia express a complete endocannabinoid signaling system and that modulation of endocannabinoid signaling alters microglial function.
- Folkes et al. (2020): Endocannabinoid Regulation of Excitatory-Inhibitory Neurotransmission . Demonstrated that increased glutamatergic activity within the basolateral amygdala–nucleus accumbens (BLA-NAc) circuit reduced social interaction and increased social avoidance. The authors further showed that 2-arachidonoylglycerol (2-AG) endocannabinoid signaling suppresses BLA-NAc glutamatergic activity, and that pharmacological augmentation of 2-AG normalized social interaction deficits in Shank3B−/− autism-model mice. Restoration of social behavior was accompanied by correction of abnormal excitatory and inhibitory neurotransmission and reduced feed-forward inhibition within the nucleus accumbens.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Li et al. (2025): Lipidomic Reorganization in Autism . Found widespread alterations across interconnected lipid pathways, including ether lipids, glycerophospholipids, and arachidonic acid-containing lipid species. The pattern is more consistent with redistribution of lipid metabolism than isolated lipid deficiency. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, these findings are consistent with an AGMO shunt in which lipid substrates are redirected across competing pathways, producing downstream effects on endocannabinoid signaling and lipid reallocation.
4. What Remains Untested
Research Gap
-
Current treatment development focuses on modulation of endocannabinoid signaling pathways rather than direct targeting of AGMO activity.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
AAAH Shunt
1. Proposed Mechanism
Mechanism
Within the model, activation of the BH4 Shunt produces the AAAH Shunt, a proposed redistribution of biochemical resources across transamination pathways and BH4-dependent neurotransmitter synthesis. The model proposes that glutamate is reallocated through transamination pathway activity while downstream dopamine and serotonin synthesis is altered according to physiological demands. Because this mechanism functions as a shunt rather than a deficiency state, neurotransmitter availability may increase or decrease depending on the type of stress, the biological resources required, and the needs of the stress response.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Zhang et al. (2024): Branched-Chain Amino Acids and Autism . Found significantly elevated concentrations of branched-chain amino acids (BCAAs), including valine and leucine/isoleucine, in autistic children. The authors note that BCAAs compete with aromatic amino acids for transport across the blood-brain barrier, potentially reducing the availability of neurotransmitter precursors required for dopamine and serotonin synthesis. This provides converging evidence of an AAAH Shunt in autism biomarkers.
4. What Remains Untested
Research Gap
- No interventions currently target the proposed AAAH Shunt mechanism directly.
- Direct AAAH pathway investigation is still needed.
- Monoamine research in autism.
- Dopamine, serotonin, and catecholamine literature.
- Downstream neurotransmitter systems are routinely targeted through existing medical interventions.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
E/I Balance in CSTL Circuitry
1. Proposed Mechanism
Mechanism
Within the model, stress-dependent reallocation of glutamate through transamination pathway activity, together with altered dopamine and serotonin synthesis, changes the biological resources available for neural circuit regulation. The model proposes that these shifts alter the balance between excitation and inhibition within corticothalamostriatal loop (CSTL) circuitry, disrupting how information is processed, filtered, and integrated across neural networks. Because excitation and inhibition function as a dynamic regulatory system, imbalance may emerge through multiple patterns of neurotransmitter reallocation. The model proposes that disruption of E/I balance within CSTL circuitry directly produces core autism traits through predictable changes in movement, habit formation, reward processing, and other region-specific neural functions depending on which CSTL circuits are most affected. Excitotoxicity due to elevated glutamate levels may also produce regressive symptoms due to damage to synapses that hold the information for how to produce skills and behaviors.
2. Prior Converging Evidence
- Sohal & Rubenstein (2019): Excitatory-Inhibitory Imbalance . Propose that altered excitatory-inhibitory (E/I) balance can reduce neural signal-to-noise ratios and contribute to autism. The authors further note that numerous developmental and genetic mechanisms may converge on E/I dysregulation. This provides converging evidence for the importance of E/I imbalance in autism. The primary difference is that Sohal and Rubenstein do not propose a converging biochemical cascade driving this imbalance, whereas Kitzerow’s Autism and the Comorbidities Theoretical Model proposes that genetic and epigenetic factors can contribute to BH4 pathway shunting, resulting in downstream neurotransmitter alterations and E/I dysregulation.
- Rubenstein & Merzenich (2003): Excitatory-Inhibitory Imbalance Hypothesis . Proposed that altered excitatory-inhibitory (E/I) balance, potentially driven by GABAergic interneuron dysfunction, contributes to autism by reducing neural signal-to-noise ratios and impairing information processing. This provides converging evidence for E/I dysregulation in autism. The primary difference is the proposed driver of the imbalance, with Kitzerow’s model implicating BH4 pathway shunting and downstream neurotransmitter alterations as an upstream mechanism.
- Bruining et al. (2020): EEG Evidence of Excitatory-Inhibitory Imbalance in Autism . Found that autistic children exhibited greater variability in functional excitatory-inhibitory (fE/I) balance and stronger long-range temporal correlations (LRTC) compared to typically developing controls. Notably, elevated fE/I and LRTC measures were observed even in autistic children with visually normal EEGs, suggesting that E/I dysregulation may be present despite the absence of conventional EEG abnormalities.
- Essa et al. (2012): Excitotoxicity and Neuronal Injury . Reviewed evidence that excessive glutamatergic activity can produce excitotoxicity, oxidative stress, mitochondrial dysfunction, and neuronal damage in autism. This provides converging evidence that neurotransmitter dysregulation can injure established neural circuits. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, because neural circuits and their synaptic connections store the information required to produce skills and behaviors, excitotoxic damage to these circuits provides a potential mechanism for regression and loss of previously acquired abilities.
- Ansary & Al-Ayadhi (2014): Glutamate Excitotoxicity and Synaptic Dysregulation . Reported elevated glutamate levels, evidence of excitotoxicity, altered GABAergic signaling, and associations with neuroinflammatory markers in autistic individuals. This provides converging evidence for excitatory-inhibitory imbalance in autism. The primary distinction is that the authors attribute the imbalance primarily to neuroinflammatory processes, whereas Kitzerow’s model proposes BH4 pathway shunting as an upstream driver of neurotransmitter dysregulation and excitotoxic stress.
- Kern et al. (2013): Neurodegeneration and Regression in Autism . Proposed that regression in autism may reflect neurodegenerative or progressive encephalopathic processes and highlighted evidence of age-related neurological deterioration in some autistic individuals. This provides converging evidence that loss of previously acquired skills may involve damage to established neural systems. Within Kitzerow’s model, regression is proposed to occur when biological stressors disrupt neural circuits and synapses that store learned skills and behaviors.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Yale study by Naples et al. (2026): mGlu5 Availability and Excitatory Neurotransmission in Autism . Found approximately 15% lower mGlu5 receptor availability across multiple brain regions in autistic individuals, with the largest differences observed in the cerebral cortex. Lower mGlu5 availability was significantly associated with EEG measures of altered excitatory neurotransmission, suggesting that reduced mGlu5 signaling may contribute to excitation-inhibition dysregulation in autism. This provides converging evidence that altered excitatory neurotransmission is a measurable biological feature of autism.
- Jang et al. (2025): Reticular Thalamic Hyperexcitability and Autism Behaviors . Found that hyperexcitability of the reticular thalamic nucleus (RT) contributed to autism-related behaviors in a Cntnap2 mouse model. Pharmacological suppression using the T-type calcium channel blocker Z944 and chemogenetic inhibition of RT activity significantly improved social deficits, repetitive behaviors, hyperactivity, and seizure-related phenotypes.
- Roh et al. (2026): NMDAR Hypofunction Rescue in SHANK2 and SHANK3 Models . Found that inhibition of the glycine transporter SLC6A20 restored NMDA receptor (NMDAR) function, rescued abnormal synaptic phospho-proteomic signaling, and normalized ASD-related behavioral phenotypes in SHANK2- and SHANK3-mutant mice. Similar restoration of NMDAR function was observed in human cortical organoids carrying SHANK2 or SHANK3 mutations.
4. What Remains Untested
Research Gap
- Existing interventions target downstream circuitry.
- Upstream mechanisms driving E/I imbalance remain largely unaddressed.
- Nature Neuroscience connectivity subtype findings.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Synaptic Pruning / mTOR Regulation
1. Proposed Mechanism
Mechanism
Within the model, synaptic pruning patterns are altered by changes in mTOR activity resulting from chronic allostasis and redox-sensitive protein regulation. Because synaptic pruning determines which neural connections are retained and which are eliminated, altered pruning patterns change the organization of neural circuitry throughout development. The model proposes that this produces predictable connectivity differences, including hyperconnectivity, hypoconnectivity, altered network specialization, and altered neural circuit maturation. While epigenetic redox-sensitive protein shunts are described within the comorbid trait mechanisms, their impact on neural circuitry places this node within the autism trait mechanisms.
2. Prior Converging Evidence
- Pagani et al. (2021): mTOR-Dependent Synaptic Pruning and Autism . Reported that postmortem studies consistently demonstrate increased density of excitatory synapses in autistic brains, a finding linked to impaired mTOR-dependent synaptic pruning. Using a Tsc2 mouse model, the authors showed that mTOR-driven increases in dendritic spine density were associated with ASD-like behaviors and functional hyperconnectivity, both of which were rescued by mTOR inhibition.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Ojha et al. (2026): Nitric Oxide-Mediated mTOR Dysregulation . Demonstrated that nitric oxide-mediated S-nitrosylation of TSC2 drives mTOR overactivation in both Shank3 and Cntnap2 autism models. This provides converging evidence that dysregulated nitric oxide signaling can function as an upstream regulator of mTOR activity, a pathway known to influence protein translation, synaptic development, and synaptic pruning.
4. What Remains Untested
Research Gap
- No treatment pathway currently targets this mechanism directly.
- Translation of mechanistic findings into interventions remains needed.
- Mechanistic convergence is emerging.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Comorbid Traits Cascade
Mechanism: neurotransmitter reallocation alters catecholamine and monoamine availability involved in mood, motivation, reward, stress response, and emotional regulation.
Mechanism: lipid reorganization alters endocannabinoid signaling involved in stress regulation, adaptation, and recovery.
Mechanism: epigenetic redox-sensitive protein shunts alter biological function across interconnected regulatory/temporal system domains.
Mechanism: interconnected regulatory/temporal system domains produce predictable patterns of comorbid trait co-occurrence.
Mechanism: prolonged activation of stress-response pathways produces cumulative biological wear and loss of functional resilience over time.
Chronic Allostasis
1. Proposed Mechanism
Mechanism
Within the model, genetic and epigenetic factors chronically activate the regulatory system domains (Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation) and disrupt the temporal system domains (Ultradian, Circadian, Circannual, Developmental, and Aging).
The model proposes that this sustained allostatic state reallocates biological resources toward resolving the stress response and away from typical development and function, producing biochemically predictable downstream changes throughout the cascade.
Persistent activation is further proposed to result in allostatic overload, contributing to the accumulation of compounding comorbid traits across regulatory domains.
2. Prior Converging Evidence
- Makris et al. (2022) . Reviewed evidence showing that autistic children and adolescents consistently demonstrate lower parasympathetic nervous system (PNS) activity alongside higher sympathetic nervous system (SNS) activity at rest.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- UCSD Dr. Naviaux’s 3-Hit Metabolic Signaling Model (2025) . Proposes that autism emerges through the interaction of genetic, chronic, and situational stressors, resulting in persistent metabolic and mitochondrial alterations that affect neurodevelopment. The model follows a similar cascade of genetic/chronic/situational factors → metabolic shift → E/I dysregulation → autism and comorbidities → developmental timing → neuroplasticity.
- Santamaría-García et al. (2025) . Allostatic Interoception and Neurodevelopment. Propose that allostatic-interoceptive processes are crucial during critical periods of neurodevelopment and that their disruption is linked to autism.
4. What Remains Untested
Research Gap
- Current approaches typically target individual pathways rather than regulatory-system domains and their interactions.
- The model predicts that restoring balance within and between regulatory domains may be more important than continuously driving individual pathways higher or lower.
- Better understanding is needed regarding biological set-point restoration across interconnected systems.
- Increasing recognition of chronic physiological stress across autism research accompanied by genetic and epigenetic factors.
- Biomedical approaches commonly target inflammation, oxidative stress, mitochondrial dysfunction, immune dysregulation, metabolic dysfunction, and other contributors to chronic physiological stress.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
BH4 Shunt
1. Proposed Mechanism
Mechanism
Within the model, chronic allostasis activates the BH4 Shunt, a proposed redox-sensitive shift in GCH1 regulation that reallocates BH4-dependent biological resources toward survival functions as a core component of the stress response system.
The model proposes that this redistribution initiates predictable downstream changes in BH4-dependent pathways involved in autism traits and comorbid traits.
As a central regulator of multiple interconnected biological systems, BH4 Shunt activation is proposed to generate cascading effects that produce autism traits, comorbid traits, and comorbidity clustering.
2. Prior Converging Evidence
- Frye (2010): BH4 Treatment in Autism . Investigated tetrahydrobiopterin (BH4) as a therapeutic intervention for autism based on its role as a critical cofactor in neurotransmitter synthesis and nitric oxide metabolism. Sixty-three percent of participants demonstrated clinical improvement following BH4 treatment. Within Kitzerow’s model, variability in treatment response may reflect underlying BH4 pathway redistribution rather than BH4 deficiency alone.
- Klaiman et al. (2013): BH4 Placebo-Controlled Trial in Autism . Found significant improvements in social awareness, autism mannerisms, hyperactivity, and inappropriate speech following BH4 treatment compared to placebo.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Filho et al. (2025): BH4 Systematic Review . Concluded that redox-regulated BH4 dysfunction impacts multiple interconnected biological systems that contribute to autism and associated comorbid traits.
4. What Remains Untested
Research Gap
- Existing interventions increase BH4 availability but do not directly target the proposed BH4 Shunt mechanism.
- The model predicts that increasing BH4 levels and modifying BH4 Shunt activation may not represent the same intervention target.
- Direct BH4 Shunt interventions have not yet been developed.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
NOS Shunt
1. Proposed Mechanism
Mechanism
Within the model, the NOS Shunt is a BH4-dependent mechanism that activates following a regulatory system set-point breach.
When coupled, nitric oxide synthase produces nitric oxide. When uncoupled, it produces reactive oxygen species (ROS). Nitric oxide and ROS can also combine to form peroxynitrite.
The model proposes that nitric oxide, ROS, and peroxynitrite function as signaling molecules that coordinate the regulatory system response across the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains.
2. Prior Converging Evidence
- Fu et al. (2019): Altered Nitric Oxide Metabolism in Autism . Reported significantly elevated urinary nitrite, reduced urinary nitrate, and increased nitrite-to-nitrate ratios in autistic children. Rather than indicating a simple increase or decrease in nitric oxide activity, the altered distribution of nitric oxide metabolites suggests disruption of nitric oxide pathway regulation. This provides converging evidence that NOS-related metabolism is altered in autism. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, such findings are consistent with NOS pathway shunting and altered downstream nitric oxide signaling.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Khan & Dewald (2024): Nitric Oxide and Peroxynitrite as Autism Biomarkers . Proposed nitric oxide and peroxynitrite as potential biomarkers for autism based on findings from induced pluripotent stem cells and brain organoids derived from autistic individuals. The study suggests that dysregulated nitric oxide signaling may be sufficiently robust to serve as a measurable biological feature of autism.
- Khan & Dewald (2026): Nitric Oxide as a Differential Diagnostic Biomarker . Using carbon fiber-based porphyrinic nanosensors and autism-derived induced pluripotent stem cells, the authors found dramatically reduced nitric oxide production in autism (~6 nM) compared to healthy controls (~65 nM), with levels also distinguishable from intellectual disability (~11 nM). The authors proposed real-time nitric oxide measurement as a potential biomarker for both diagnosis and differential diagnosis of autism.
4. What Remains Untested
Research Gap
- No interventions currently target the proposed NOS Shunt directly.
- Direct NOS Shunt interventions remain needed.
- Research is increasingly identifying downstream effects of nitric oxide dysregulation.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Epigenetic Redox-Sensitive Protein Shunts
1. Proposed Mechanism
Mechanism
Within the model, epigenetic redox-sensitive protein shunts function as regulatory system effectors that activate in response to ROS signaling following a regulatory system set-point breach.
The model proposes that these effectors alter pathway activity within the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains to support adaptation to ongoing physiological demands.
These alterations in pathway activity are proposed to produce comorbid traits.
2. Prior Converging Evidence
- Pagani et al. (2021): mTOR-Dependent Synaptic Pruning and Autism . Reported that postmortem studies consistently demonstrate increased density of excitatory synapses in autistic brains, a finding linked to impaired mTOR-dependent synaptic pruning. Using a Tsc2 mouse model, the authors showed that mTOR-driven increases in dendritic spine density were associated with ASD-like behaviors and functional hyperconnectivity, both of which were rescued by mTOR inhibition.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Ojha et al. (2026): Nitric Oxide-Mediated mTOR Dysregulation . Demonstrated that nitric oxide-mediated S-nitrosylation of TSC2 drives mTOR overactivation in both Shank3 and Cntnap2 autism models. This provides converging evidence that dysregulated nitric oxide signaling can function as an upstream regulator of mTOR activity.
4. What Remains Untested
Research Gap
- Additional epigenetic redox-sensitive protein shunts likely remain unidentified.
- Mechanism-specific interventions have not yet been developed.
- No targeted interventions currently identified.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Allostatic Overload
1. Proposed Mechanism
Mechanism
Within the model, allostatic overload occurs when regulatory system set points fail to restore to baseline and allostatic mechanisms remain active for prolonged periods of time.
The model proposes that prolonged activation of these allostatic mechanisms creates cumulative biological wear across the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains.
As biological resources continue to be prioritized toward adaptation and stress resolution, maintenance and repair functions become increasingly compromised. The model proposes that this biological burden contributes to the development of chronic progressive comorbid conditions.
2. Prior Converging Evidence
- Nadeem et al. (2021): Overlapping Neurodevelopmental and Neurodegenerative Pathways . Identified shared molecular mechanisms between autism and Alzheimer’s disease involving APP processing, protein regulation, and cellular signaling networks. This provides converging evidence that pathways implicated in autism can overlap with mechanisms associated with long-term neurodegeneration. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, such overlap is consistent with the hypothesis that prolonged dysregulation across interconnected biological systems may contribute to cumulative physiological burden and the development of chronic progressive comorbid conditions.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Phillips et al. (2026): Shared Cerebrospinal Fluid Clearance Abnormalities in Autism and Alzheimer’s Disease . Proposed that impaired cerebrospinal fluid (CSF) drainage through lymphatic, glymphatic, perivascular, and nasal clearance pathways may contribute to both autism and Alzheimer’s disease. The authors highlight evidence that individuals with ASD and AD exhibit increased extra-axial CSF, enlarged perivascular spaces, glymphatic dysfunction, olfactory abnormalities, and altered processing of tau and amyloid proteins. This provides converging evidence that autism and neurodegenerative disorders may share mechanisms involving impaired biological waste clearance and accumulation of cellular burden. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, these findings are consistent with the concept that prolonged dysregulation across interconnected regulatory systems can produce cumulative physiological burden over time.
4. What Remains Untested
Research Gap
- Better understanding of how overload progresses across interconnected regulatory domains.
- Neurodegenerative and neuromuscular autism comorbidities.
- Primarily symptom management.
- Domain-specific interventions.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Comorbidity Clustering
1. Proposed Mechanism
Mechanism
Within the model, comorbidity clustering emerges from the interconnected nature of BH4-dependent pathways and the regulatory system domains.
The model proposes that activation of the BH4 Shunt produces coordinated changes across the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains because these systems do not function independently.
As pathway activity shifts within one domain, downstream effects can propagate throughout the broader regulatory network.
The model proposes that this biological interconnectedness results in predictable patterns of co-occurring traits and conditions rather than isolated comorbidities.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Litman et al. (2025) . Princeton researchers demonstrated that phenotypic and clinical outcomes correspond to genetic and molecular programs of common, de novo, and inherited variation and further characterized distinct biological pathways disrupted by each class of mutation. These findings provide converging evidence that autism traits and comorbid traits may emerge through coordinated biological programs rather than isolated genetic effects, consistent with the model's prediction of comorbidity clustering across interconnected regulatory domains.
4. What Remains Untested
Research Gap
- Predictive clustering models.
- Cluster-specific interventions.
- Earlier identification of regulatory patterns before multiple comorbid traits emerge.
- Autism-comorbidity clustering observations.
- Symptom-specific treatment approaches.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Regulatory System Domains
1. Proposed Mechanism
Within the model, comorbid traits emerge when epigenetic redox-sensitive protein shunts alter pathway activity within specific regulatory system domains. These protein shunts function as regulatory system effectors that modify biological activity following a set-point breach.
Because each domain contains distinct pathways, proteins, and biological functions, activation of different protein shunts can produce different comorbid traits. The examples below highlight representative mechanisms that may contribute to trait development within each domain.
The model organizes these effects into five interconnected regulatory system domains: Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation. While separated for clarity, these domains interact continuously and changes within one domain may propagate throughout the broader regulatory network.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
The remaining validation requirements have not yet been specified.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Immune System Domain
1. Proposed Mechanism
KEAP1 Shunt
The KEAP1 Shunt regulates antioxidant defense activation through the KEAP1-NRF2 pathway.
ROS-mediated activation shifts biological resources toward glutathione synthesis, detoxification, redox buffering, and damage control.
The model proposes that prolonged activation alters immune signaling, inflammatory regulation, and metabolic allocation, producing immune-related comorbid traits.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- Mechanism-specific interventions.
- Antioxidant response
- Inflammatory signaling
- Immune tolerance alterations
- No targeted interventions currently identified.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Metabolism Domain
1. Proposed Mechanism
MTR Shunt
The MTR Shunt regulates one-carbon metabolism and methylation capacity.
The model proposes that MTR activity reallocates methylfolate, cobalamin, homocysteine, and transsulfuration pathway resources according to metabolic demands.
These shifts alter methylation, glutathione production, energy metabolism, and redox regulation, producing metabolic comorbid traits.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- Direct BH4 Shunt interventions.
- Methylation changes
- Glutathione regulation
- Homocysteine metabolism
- Energy production
- Pathway-specific nutritional and metabolic interventions.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Cellular Repair Domain
1. Proposed Mechanism
MMP Shunt
The MMP Shunt regulates extracellular matrix turnover and remodeling.
The model proposes that altered MMP activity shifts cellular resources between tissue maintenance, repair, and degradation.
Persistent dysregulation may impair connective tissue integrity and structural maintenance, producing cellular repair-related comorbid traits.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- MMP-targeted therapies.
- hEDS
- Connective tissue dysfunction
- Extracellular matrix remodeling
- Chronic pain syndromes
- Primarily symptom-based management approaches.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Nervous System Domain
1. Proposed Mechanism
CRH Shunt
The CRH Shunt regulates neuroimmune stress signaling through activation of the hypothalamic-pituitary-adrenal (HPA) axis.
Kitzerow’s Autism and the Comorbidities Cascade Progress Tracker
Tracking the progression of the hypothesis through biological validation and eventual translation.
2023
2026
The scale summarizes overall progress. Individual nodes are graded separately in the tracker below.
Biological Coherence
What This Tracker Evaluates
The central question is whether independently observed findings increasingly align with the biological relationships predicted by the Cascade. Every node follows the same sequence so readers can distinguish the proposed mechanism, its prior scientific foundation, subsequent findings, and the work still required for validation.
Optional translation outlookHow Validated Nodes Could Eventually TranslateExplore the five potential levels from downstream trait management to upstream biological targets.
Translation Outlook
How Validated Nodes Could Eventually Translate
The five BioToggle® levels show how validated biological nodes could eventually support increasingly upstream measurement and intervention. They are a translational map, not evidence that every proposed node has already been validated or can currently be treated.
From Validation to Translation
Five Potential Translation Levels
BioToggle® maps how biological coherence could eventually translate from downstream trait management toward upstream, node-specific intervention. Progress depends first on validating which nodes are active, how they relate, and which predicted effects they produce. Because interacting variables differ, the relevant node or combination of nodes may also differ among individuals.
Treat the Individual Trait
This is how most treatment currently works. A downstream medical or physiological trait develops and that trait is treated directly.
- Sleep problems are treated as sleep problems.
- Seizures are treated as seizures.
- Gastrointestinal problems are treated individually.
- Mental health traits are treated through their downstream systems.
- Autonomic and metabolic traits are managed according to the affected system.
These interventions may improve the individual trait without changing the upstream mechanism that produced it.
Target a Downstream Pathway
Treatment can move one level upstream by targeting a pathway known to contribute to one or more downstream traits.
- Neurotransmitter signaling
- Excitation and inhibition balance
- mTOR activity
- Endocannabinoid signaling
- Immune and inflammatory pathways
- Oxidative stress and metabolic pathways
This may influence several downstream effects, but treatment is still occurring below the proposed mechanism responsible for shifting pathway activity.
Target the Shunt Producing the Pathway Change
The Cascade proposes that many downstream pathway abnormalities are not isolated failures. They reflect changes in how biological resources are allocated between competing pathways according to physiological demand.
- BH4 Shunt
- AAAH Shunt
- NOS Shunt
- AGMO Shunt
- Epigenetic redox-sensitive protein shunts
At this level, the treatment question changes from “How do we increase or decrease this pathway?” to “Why is biological activity being redirected in the first place?”
Restore Regulatory-System Balance
BioToggle® proposes that the shunts occur within a larger regulatory response involving the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains.
These systems interact continuously to maintain biological balance and restore set points after physiological demands change.
Treatment at this level would focus on why a set point was breached, what is preventing restoration, and how balance can be restored within and between interconnected regulatory systems.
Target the Upstream Genetic or Protein Driver
Genetic variation can alter protein function upstream of pathway and regulatory-system activity.
Treatment at this level moves closest to the beginning of the biological Cascade.
Gene, RNA, and protein-directed approaches could potentially modify an upstream biological driver before its effects propagate throughout the downstream cascade.
Why Move Upstream?
The farther upstream a shared mechanism sits, the more downstream effects it may influence. That is why identifying shared biology matters for understanding and eventually treating comorbidity clustering.
Comorbidity Clustering Changes the Treatment Question
Comorbidity clustering means multiple traits repeatedly occur together rather than appearing as completely unrelated conditions.
If five traits are produced by five unrelated mechanisms, they may require five different treatments. If those traits share one upstream mechanism, that shared mechanism creates an additional treatment target.
The goal is therefore not only to identify which comorbid conditions occur in autism. It is to determine which traits cluster together, what biology they share, where those pathways converge, and whether that shared mechanism can be targeted.
Model-level falsifiabilityWhat Would Falsify the Cascade?See the evidence that could disconfirm the Cascade's predictions.
The Cascade does not predict that every autistic individual will have the same biomarkers, the same most-upstream identifiable node, or the same cluster of autism and comorbid traits. Individual variation is expected in which biological systems are affected, when dysregulation occurs, how long it persists, and how biological resources are allocated throughout the Cascade.
The Cascade also includes feedback, reinforcing, inhibitory, and compensatory relationships. It does not require every biological interaction to operate in only one direction.
The model predicts that measurable dysregulation within the Cascade will correspond to specific downstream biological effects and distinguishable clusters of autism and comorbid traits. The Cascade would be challenged if those predicted biomarker patterns, trait classifications, or biological mechanisms repeatedly failed when tested in relevant populations and datasets.
Each node can be falsified in three primary ways:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by that node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For upstream nodes, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect is demonstrated to be biologically inaccurate.
Failure at one node may require revision of that node, its assigned traits, or its relationship to the rest of the Cascade. Whole-Cascade falsification would require repeated failure of the broader prediction that dysregulation within the Cascade produces biologically traceable and distinguishable trait patterns.
Current Progress
2023 → 2026Two Connected Validation Tracks
Why the Model Is Separated Into Two Cascades
The two Cascades share proposed upstream biology, but they follow that biology toward different predicted outcomes. Separating them makes it possible to evaluate whether each node is connected to the specific type of trait assigned to it without implying that autism traits and comorbid traits are interchangeable.
Tracks predicted effects on neural circuitry, neural connectivity, skill and behavior development, regression, and social-interaction or social-avoidance traits.
Tracks predicted medical and physiological effects, regulatory-system traits, comorbidity clustering, allostatic overload, and chronic progressive conditions.
The Cascades are separated for validation, not because they operate independently. A shared upstream node may contribute to both tracks, while downstream branches require different evidence, outcome measures, and eventual forms of translation.
Autism Traits Cascade
Mechanism: Regulatory system domain activation (immune system, metabolism, cellular repair, nervous system and genetic regulation. Either due to situational activation that becomes chronically impactful due to overload or genetic factors.
Mechanism: CSTL excitation/inhibition imbalance disrupts the formation and activity within circuits that produce movement, habit formation, and reward. Driving dysregulation in skill/behavior development. Excitotoxic synaptic damage impacts memories for how to produce skills and behaviors.
Mechanism: mTOR-mediated synaptic pruning dysregulation alters circuit refinement and connectivity patterns.
Mechanism: lipid reorganization alters endocannabinoid signaling, microglial activity, and social-regulatory pathway activity.
Chronic Allostasis
1. Proposed Mechanism
Within the model, genetic and epigenetic factors chronically activate the regulatory system domains (Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation) and disrupt the temporal system domains (Ultradian, Circadian, Circannual, Developmental, and Aging). The model proposes that this sustained allostatic state reallocates biological resources toward resolving the stress response and away from typical development and function, producing biochemically predictable downstream changes throughout the cascade. Persistent activation is further proposed to result in allostatic overload, contributing to the accumulation of compounding comorbid traits across regulatory domains.
2. Prior Converging Evidence
- Makris et al. (2022) . Autonomic Nervous System Dysregulation in Autism.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- UCSD Dr. Naviaux’s 3-Hit Metabolic Signaling Model (2025) . Proposes that autism emerges through the interaction of genetic, chronic, and situational stressors...
- Santamaría-García et al. (2025) . Allostatic Interoception and Neurodevelopment.
4. What Remains Untested
- Biomedical approaches commonly target inflammation, oxidative stress, mitochondrial dysfunction, immune dysregulation, metabolic dysfunction, and other contributors to chronic physiological stress.
- Current approaches typically target individual pathways rather than regulatory-system domains and their interactions.
- The model predicts that restoring balance within and between regulatory domains may be more important than continuously driving individual pathways higher or lower.
- Better understanding is needed regarding biological set-point restoration across interconnected systems.
- Increasing recognition of chronic physiological stress across autism research accompanied by genetic and epigenetic factors.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
BH4 Shunt
1. Proposed Mechanism
Mechanism
Within the model, chronic allostasis activates the BH4 Shunt, a proposed redox-sensitive shift in GCH1 regulation that reallocates BH4-dependent biological resources toward survival functions as a core component of the stress response system. The model proposes that this redistribution initiates predictable downstream changes in BH4-dependent pathways involved in autism traits and comorbid traits. As a central regulator of multiple interconnected biological systems, BH4 Shunt activation is proposed to generate cascading effects that produce autism traits, comorbid traits, and comorbidity clustering.
2. Prior Converging Evidence
- Frye (2010): BH4 Treatment in Autism. Investigated tetrahydrobiopterin (BH4) as a therapeutic intervention for autism based on its role as a critical cofactor in neurotransmitter synthesis and nitric oxide metabolism. Sixty-three percent of participants demonstrated clinical improvement following BH4 treatment. The study approached BH4 dysfunction as a deficiency state rather than a BH4 shunt mechanism. Within Kitzerow’s model, variability in treatment response may reflect underlying BH4 pathway redistribution rather than BH4 deficiency alone.
- Klaiman et al. (2013): BH4 Placebo-Controlled Trial in Autism. Found significant improvements in social awareness, autism mannerisms, hyperactivity, and inappropriate speech following BH4 treatment compared to placebo.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Filho et al. (2025): BH4 Systematic Review. Concluded that redox-regulated BH4 dysfunction impacts multiple interconnected biological systems that contribute to autism and associated comorbid traits.
4. What Remains Untested
Research Gap
- Existing interventions increase BH4 availability but do not directly target the proposed BH4 Shunt mechanism.
- The model predicts that increasing BH4 levels and modifying BH4 Shunt activation may not represent the same intervention target.
- Direct BH4 Shunt interventions have not yet been developed.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
AGMO Shunt
1. Proposed Mechanism
Mechanism
Within the model, the AGMO Shunt is a BH4-dependent lipid-regulation mechanism that activates following a regulatory system set-point breach. AGMO regulates the catabolism of alkylglycerol ether lipids. Ether lipids include plasmanyl phospholipids, plasmalogens, platelet-activating factor (PAF), and endocannabinoid-related lipids such as noladin ether. The model proposes that changes in AGMO pathway activity alter lipid signaling and regulatory function across biological systems, producing comorbid traits. The specific traits produced by AGMO pathway dysregulation appear to be involved in social interaction and social avoidance.
2. Prior Converging Evidence
- Dorninger et al. (2019): Ether Lipid Deficiency and Neurotransmitter Homeostasis . Using a Gnpat knockout mouse model of ether lipid deficiency, the authors demonstrated reduced neurotransmitter levels, altered neurotransmitter turnover, impaired vesicular monoamine transport, reduced neurotransmitter release, hyperactivity, and impaired social interaction.
- Smith et al. (2023): Ether Lipid and Glycerophospholipid Dysregulation in Autism . Identified reproducible alterations in ether lipid subclasses, glycerophospholipid domains, and long-chain polyunsaturated fatty acids including linoleic acid, arachidonic acid, and DHA in autistic individuals. This provides converging evidence that lipid pathways involved in membrane composition, ether lipid metabolism, and endocannabinoid precursor availability are altered in autism.
- Su et al. (2021): Endocannabinoid Signaling, Social Reward, and Microglial Regulation . Reviewed evidence demonstrating that the endocannabinoid system participates in the regulation of social reward behavior and has emerged as a potential therapeutic target in autism. The authors further note that microglia express a complete endocannabinoid signaling system and that modulation of endocannabinoid signaling alters microglial function.
- Folkes et al. (2020): Endocannabinoid Regulation of Excitatory-Inhibitory Neurotransmission . Demonstrated that increased glutamatergic activity within the basolateral amygdala–nucleus accumbens (BLA-NAc) circuit reduced social interaction and increased social avoidance. The authors further showed that 2-arachidonoylglycerol (2-AG) endocannabinoid signaling suppresses BLA-NAc glutamatergic activity, and that pharmacological augmentation of 2-AG normalized social interaction deficits in Shank3B−/− autism-model mice. Restoration of social behavior was accompanied by correction of abnormal excitatory and inhibitory neurotransmission and reduced feed-forward inhibition within the nucleus accumbens.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Li et al. (2025): Lipidomic Reorganization in Autism . Found widespread alterations across interconnected lipid pathways, including ether lipids, glycerophospholipids, and arachidonic acid-containing lipid species. The pattern is more consistent with redistribution of lipid metabolism than isolated lipid deficiency. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, these findings are consistent with an AGMO shunt in which lipid substrates are redirected across competing pathways, producing downstream effects on endocannabinoid signaling and lipid reallocation.
4. What Remains Untested
Research Gap
-
Current treatment development focuses on modulation of endocannabinoid signaling pathways rather than direct targeting of AGMO activity.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
AAAH Shunt
1. Proposed Mechanism
Mechanism
Within the model, activation of the BH4 Shunt produces the AAAH Shunt, a proposed redistribution of biochemical resources across transamination pathways and BH4-dependent neurotransmitter synthesis. The model proposes that glutamate is reallocated through transamination pathway activity while downstream dopamine and serotonin synthesis is altered according to physiological demands. Because this mechanism functions as a shunt rather than a deficiency state, neurotransmitter availability may increase or decrease depending on the type of stress, the biological resources required, and the needs of the stress response.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Zhang et al. (2024): Branched-Chain Amino Acids and Autism . Found significantly elevated concentrations of branched-chain amino acids (BCAAs), including valine and leucine/isoleucine, in autistic children. The authors note that BCAAs compete with aromatic amino acids for transport across the blood-brain barrier, potentially reducing the availability of neurotransmitter precursors required for dopamine and serotonin synthesis. This provides converging evidence of an AAAH Shunt in autism biomarkers.
4. What Remains Untested
Research Gap
- No interventions currently target the proposed AAAH Shunt mechanism directly.
- Direct AAAH pathway investigation is still needed.
- Monoamine research in autism.
- Dopamine, serotonin, and catecholamine literature.
- Downstream neurotransmitter systems are routinely targeted through existing medical interventions.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
E/I Balance in CSTL Circuitry
1. Proposed Mechanism
Mechanism
Within the model, stress-dependent reallocation of glutamate through transamination pathway activity, together with altered dopamine and serotonin synthesis, changes the biological resources available for neural circuit regulation. The model proposes that these shifts alter the balance between excitation and inhibition within corticothalamostriatal loop (CSTL) circuitry, disrupting how information is processed, filtered, and integrated across neural networks. Because excitation and inhibition function as a dynamic regulatory system, imbalance may emerge through multiple patterns of neurotransmitter reallocation. The model proposes that disruption of E/I balance within CSTL circuitry directly produces core autism traits through predictable changes in movement, habit formation, reward processing, and other region-specific neural functions depending on which CSTL circuits are most affected. Excitotoxicity due to elevated glutamate levels may also produce regressive symptoms due to damage to synapses that hold the information for how to produce skills and behaviors.
2. Prior Converging Evidence
- Sohal & Rubenstein (2019): Excitatory-Inhibitory Imbalance . Propose that altered excitatory-inhibitory (E/I) balance can reduce neural signal-to-noise ratios and contribute to autism. The authors further note that numerous developmental and genetic mechanisms may converge on E/I dysregulation. This provides converging evidence for the importance of E/I imbalance in autism. The primary difference is that Sohal and Rubenstein do not propose a converging biochemical cascade driving this imbalance, whereas Kitzerow’s Autism and the Comorbidities Theoretical Model proposes that genetic and epigenetic factors can contribute to BH4 pathway shunting, resulting in downstream neurotransmitter alterations and E/I dysregulation.
- Rubenstein & Merzenich (2003): Excitatory-Inhibitory Imbalance Hypothesis . Proposed that altered excitatory-inhibitory (E/I) balance, potentially driven by GABAergic interneuron dysfunction, contributes to autism by reducing neural signal-to-noise ratios and impairing information processing. This provides converging evidence for E/I dysregulation in autism. The primary difference is the proposed driver of the imbalance, with Kitzerow’s model implicating BH4 pathway shunting and downstream neurotransmitter alterations as an upstream mechanism.
- Bruining et al. (2020): EEG Evidence of Excitatory-Inhibitory Imbalance in Autism . Found that autistic children exhibited greater variability in functional excitatory-inhibitory (fE/I) balance and stronger long-range temporal correlations (LRTC) compared to typically developing controls. Notably, elevated fE/I and LRTC measures were observed even in autistic children with visually normal EEGs, suggesting that E/I dysregulation may be present despite the absence of conventional EEG abnormalities.
- Essa et al. (2012): Excitotoxicity and Neuronal Injury . Reviewed evidence that excessive glutamatergic activity can produce excitotoxicity, oxidative stress, mitochondrial dysfunction, and neuronal damage in autism. This provides converging evidence that neurotransmitter dysregulation can injure established neural circuits. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, because neural circuits and their synaptic connections store the information required to produce skills and behaviors, excitotoxic damage to these circuits provides a potential mechanism for regression and loss of previously acquired abilities.
- Ansary & Al-Ayadhi (2014): Glutamate Excitotoxicity and Synaptic Dysregulation . Reported elevated glutamate levels, evidence of excitotoxicity, altered GABAergic signaling, and associations with neuroinflammatory markers in autistic individuals. This provides converging evidence for excitatory-inhibitory imbalance in autism. The primary distinction is that the authors attribute the imbalance primarily to neuroinflammatory processes, whereas Kitzerow’s model proposes BH4 pathway shunting as an upstream driver of neurotransmitter dysregulation and excitotoxic stress.
- Kern et al. (2013): Neurodegeneration and Regression in Autism . Proposed that regression in autism may reflect neurodegenerative or progressive encephalopathic processes and highlighted evidence of age-related neurological deterioration in some autistic individuals. This provides converging evidence that loss of previously acquired skills may involve damage to established neural systems. Within Kitzerow’s model, regression is proposed to occur when biological stressors disrupt neural circuits and synapses that store learned skills and behaviors.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Yale study by Naples et al. (2026): mGlu5 Availability and Excitatory Neurotransmission in Autism . Found approximately 15% lower mGlu5 receptor availability across multiple brain regions in autistic individuals, with the largest differences observed in the cerebral cortex. Lower mGlu5 availability was significantly associated with EEG measures of altered excitatory neurotransmission, suggesting that reduced mGlu5 signaling may contribute to excitation-inhibition dysregulation in autism. This provides converging evidence that altered excitatory neurotransmission is a measurable biological feature of autism.
- Jang et al. (2025): Reticular Thalamic Hyperexcitability and Autism Behaviors . Found that hyperexcitability of the reticular thalamic nucleus (RT) contributed to autism-related behaviors in a Cntnap2 mouse model. Pharmacological suppression using the T-type calcium channel blocker Z944 and chemogenetic inhibition of RT activity significantly improved social deficits, repetitive behaviors, hyperactivity, and seizure-related phenotypes.
- Roh et al. (2026): NMDAR Hypofunction Rescue in SHANK2 and SHANK3 Models . Found that inhibition of the glycine transporter SLC6A20 restored NMDA receptor (NMDAR) function, rescued abnormal synaptic phospho-proteomic signaling, and normalized ASD-related behavioral phenotypes in SHANK2- and SHANK3-mutant mice. Similar restoration of NMDAR function was observed in human cortical organoids carrying SHANK2 or SHANK3 mutations.
4. What Remains Untested
Research Gap
- Existing interventions target downstream circuitry.
- Upstream mechanisms driving E/I imbalance remain largely unaddressed.
- Nature Neuroscience connectivity subtype findings.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Synaptic Pruning / mTOR Regulation
1. Proposed Mechanism
Mechanism
Within the model, synaptic pruning patterns are altered by changes in mTOR activity resulting from chronic allostasis and redox-sensitive protein regulation. Because synaptic pruning determines which neural connections are retained and which are eliminated, altered pruning patterns change the organization of neural circuitry throughout development. The model proposes that this produces predictable connectivity differences, including hyperconnectivity, hypoconnectivity, altered network specialization, and altered neural circuit maturation. While epigenetic redox-sensitive protein shunts are described within the comorbid trait mechanisms, their impact on neural circuitry places this node within the autism trait mechanisms.
2. Prior Converging Evidence
- Pagani et al. (2021): mTOR-Dependent Synaptic Pruning and Autism . Reported that postmortem studies consistently demonstrate increased density of excitatory synapses in autistic brains, a finding linked to impaired mTOR-dependent synaptic pruning. Using a Tsc2 mouse model, the authors showed that mTOR-driven increases in dendritic spine density were associated with ASD-like behaviors and functional hyperconnectivity, both of which were rescued by mTOR inhibition.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Ojha et al. (2026): Nitric Oxide-Mediated mTOR Dysregulation . Demonstrated that nitric oxide-mediated S-nitrosylation of TSC2 drives mTOR overactivation in both Shank3 and Cntnap2 autism models. This provides converging evidence that dysregulated nitric oxide signaling can function as an upstream regulator of mTOR activity, a pathway known to influence protein translation, synaptic development, and synaptic pruning.
4. What Remains Untested
Research Gap
- No treatment pathway currently targets this mechanism directly.
- Translation of mechanistic findings into interventions remains needed.
- Mechanistic convergence is emerging.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Comorbid Traits Cascade
Mechanism: neurotransmitter reallocation alters catecholamine and monoamine availability involved in mood, motivation, reward, stress response, and emotional regulation.
Mechanism: lipid reorganization alters endocannabinoid signaling involved in stress regulation, adaptation, and recovery.
Mechanism: epigenetic redox-sensitive protein shunts alter biological function across interconnected regulatory/temporal system domains.
Mechanism: interconnected regulatory/temporal system domains produce predictable patterns of comorbid trait co-occurrence.
Mechanism: prolonged activation of stress-response pathways produces cumulative biological wear and loss of functional resilience over time.
Chronic Allostasis
1. Proposed Mechanism
Mechanism
Within the model, genetic and epigenetic factors chronically activate the regulatory system domains (Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation) and disrupt the temporal system domains (Ultradian, Circadian, Circannual, Developmental, and Aging).
The model proposes that this sustained allostatic state reallocates biological resources toward resolving the stress response and away from typical development and function, producing biochemically predictable downstream changes throughout the cascade.
Persistent activation is further proposed to result in allostatic overload, contributing to the accumulation of compounding comorbid traits across regulatory domains.
2. Prior Converging Evidence
- Makris et al. (2022) . Reviewed evidence showing that autistic children and adolescents consistently demonstrate lower parasympathetic nervous system (PNS) activity alongside higher sympathetic nervous system (SNS) activity at rest.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- UCSD Dr. Naviaux’s 3-Hit Metabolic Signaling Model (2025) . Proposes that autism emerges through the interaction of genetic, chronic, and situational stressors, resulting in persistent metabolic and mitochondrial alterations that affect neurodevelopment. The model follows a similar cascade of genetic/chronic/situational factors → metabolic shift → E/I dysregulation → autism and comorbidities → developmental timing → neuroplasticity.
- Santamaría-García et al. (2025) . Allostatic Interoception and Neurodevelopment. Propose that allostatic-interoceptive processes are crucial during critical periods of neurodevelopment and that their disruption is linked to autism.
4. What Remains Untested
Research Gap
- Current approaches typically target individual pathways rather than regulatory-system domains and their interactions.
- The model predicts that restoring balance within and between regulatory domains may be more important than continuously driving individual pathways higher or lower.
- Better understanding is needed regarding biological set-point restoration across interconnected systems.
- Increasing recognition of chronic physiological stress across autism research accompanied by genetic and epigenetic factors.
- Biomedical approaches commonly target inflammation, oxidative stress, mitochondrial dysfunction, immune dysregulation, metabolic dysfunction, and other contributors to chronic physiological stress.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
BH4 Shunt
1. Proposed Mechanism
Mechanism
Within the model, chronic allostasis activates the BH4 Shunt, a proposed redox-sensitive shift in GCH1 regulation that reallocates BH4-dependent biological resources toward survival functions as a core component of the stress response system.
The model proposes that this redistribution initiates predictable downstream changes in BH4-dependent pathways involved in autism traits and comorbid traits.
As a central regulator of multiple interconnected biological systems, BH4 Shunt activation is proposed to generate cascading effects that produce autism traits, comorbid traits, and comorbidity clustering.
2. Prior Converging Evidence
- Frye (2010): BH4 Treatment in Autism . Investigated tetrahydrobiopterin (BH4) as a therapeutic intervention for autism based on its role as a critical cofactor in neurotransmitter synthesis and nitric oxide metabolism. Sixty-three percent of participants demonstrated clinical improvement following BH4 treatment. Within Kitzerow’s model, variability in treatment response may reflect underlying BH4 pathway redistribution rather than BH4 deficiency alone.
- Klaiman et al. (2013): BH4 Placebo-Controlled Trial in Autism . Found significant improvements in social awareness, autism mannerisms, hyperactivity, and inappropriate speech following BH4 treatment compared to placebo.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Filho et al. (2025): BH4 Systematic Review . Concluded that redox-regulated BH4 dysfunction impacts multiple interconnected biological systems that contribute to autism and associated comorbid traits.
4. What Remains Untested
Research Gap
- Existing interventions increase BH4 availability but do not directly target the proposed BH4 Shunt mechanism.
- The model predicts that increasing BH4 levels and modifying BH4 Shunt activation may not represent the same intervention target.
- Direct BH4 Shunt interventions have not yet been developed.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
NOS Shunt
1. Proposed Mechanism
Mechanism
Within the model, the NOS Shunt is a BH4-dependent mechanism that activates following a regulatory system set-point breach.
When coupled, nitric oxide synthase produces nitric oxide. When uncoupled, it produces reactive oxygen species (ROS). Nitric oxide and ROS can also combine to form peroxynitrite.
The model proposes that nitric oxide, ROS, and peroxynitrite function as signaling molecules that coordinate the regulatory system response across the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains.
2. Prior Converging Evidence
- Fu et al. (2019): Altered Nitric Oxide Metabolism in Autism . Reported significantly elevated urinary nitrite, reduced urinary nitrate, and increased nitrite-to-nitrate ratios in autistic children. Rather than indicating a simple increase or decrease in nitric oxide activity, the altered distribution of nitric oxide metabolites suggests disruption of nitric oxide pathway regulation. This provides converging evidence that NOS-related metabolism is altered in autism. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, such findings are consistent with NOS pathway shunting and altered downstream nitric oxide signaling.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Khan & Dewald (2024): Nitric Oxide and Peroxynitrite as Autism Biomarkers . Proposed nitric oxide and peroxynitrite as potential biomarkers for autism based on findings from induced pluripotent stem cells and brain organoids derived from autistic individuals. The study suggests that dysregulated nitric oxide signaling may be sufficiently robust to serve as a measurable biological feature of autism.
- Khan & Dewald (2026): Nitric Oxide as a Differential Diagnostic Biomarker . Using carbon fiber-based porphyrinic nanosensors and autism-derived induced pluripotent stem cells, the authors found dramatically reduced nitric oxide production in autism (~6 nM) compared to healthy controls (~65 nM), with levels also distinguishable from intellectual disability (~11 nM). The authors proposed real-time nitric oxide measurement as a potential biomarker for both diagnosis and differential diagnosis of autism.
4. What Remains Untested
Research Gap
- No interventions currently target the proposed NOS Shunt directly.
- Direct NOS Shunt interventions remain needed.
- Research is increasingly identifying downstream effects of nitric oxide dysregulation.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Epigenetic Redox-Sensitive Protein Shunts
1. Proposed Mechanism
Mechanism
Within the model, epigenetic redox-sensitive protein shunts function as regulatory system effectors that activate in response to ROS signaling following a regulatory system set-point breach.
The model proposes that these effectors alter pathway activity within the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains to support adaptation to ongoing physiological demands.
These alterations in pathway activity are proposed to produce comorbid traits.
2. Prior Converging Evidence
- Pagani et al. (2021): mTOR-Dependent Synaptic Pruning and Autism . Reported that postmortem studies consistently demonstrate increased density of excitatory synapses in autistic brains, a finding linked to impaired mTOR-dependent synaptic pruning. Using a Tsc2 mouse model, the authors showed that mTOR-driven increases in dendritic spine density were associated with ASD-like behaviors and functional hyperconnectivity, both of which were rescued by mTOR inhibition.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Ojha et al. (2026): Nitric Oxide-Mediated mTOR Dysregulation . Demonstrated that nitric oxide-mediated S-nitrosylation of TSC2 drives mTOR overactivation in both Shank3 and Cntnap2 autism models. This provides converging evidence that dysregulated nitric oxide signaling can function as an upstream regulator of mTOR activity.
4. What Remains Untested
Research Gap
- Additional epigenetic redox-sensitive protein shunts likely remain unidentified.
- Mechanism-specific interventions have not yet been developed.
- No targeted interventions currently identified.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Allostatic Overload
1. Proposed Mechanism
Mechanism
Within the model, allostatic overload occurs when regulatory system set points fail to restore to baseline and allostatic mechanisms remain active for prolonged periods of time.
The model proposes that prolonged activation of these allostatic mechanisms creates cumulative biological wear across the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains.
As biological resources continue to be prioritized toward adaptation and stress resolution, maintenance and repair functions become increasingly compromised. The model proposes that this biological burden contributes to the development of chronic progressive comorbid conditions.
2. Prior Converging Evidence
- Nadeem et al. (2021): Overlapping Neurodevelopmental and Neurodegenerative Pathways . Identified shared molecular mechanisms between autism and Alzheimer’s disease involving APP processing, protein regulation, and cellular signaling networks. This provides converging evidence that pathways implicated in autism can overlap with mechanisms associated with long-term neurodegeneration. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, such overlap is consistent with the hypothesis that prolonged dysregulation across interconnected biological systems may contribute to cumulative physiological burden and the development of chronic progressive comorbid conditions.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Phillips et al. (2026): Shared Cerebrospinal Fluid Clearance Abnormalities in Autism and Alzheimer’s Disease . Proposed that impaired cerebrospinal fluid (CSF) drainage through lymphatic, glymphatic, perivascular, and nasal clearance pathways may contribute to both autism and Alzheimer’s disease. The authors highlight evidence that individuals with ASD and AD exhibit increased extra-axial CSF, enlarged perivascular spaces, glymphatic dysfunction, olfactory abnormalities, and altered processing of tau and amyloid proteins. This provides converging evidence that autism and neurodegenerative disorders may share mechanisms involving impaired biological waste clearance and accumulation of cellular burden. Within Kitzerow’s Autism and the Comorbidities Theoretical Model, these findings are consistent with the concept that prolonged dysregulation across interconnected regulatory systems can produce cumulative physiological burden over time.
4. What Remains Untested
Research Gap
- Better understanding of how overload progresses across interconnected regulatory domains.
- Neurodegenerative and neuromuscular autism comorbidities.
- Primarily symptom management.
- Domain-specific interventions.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Comorbidity Clustering
1. Proposed Mechanism
Mechanism
Within the model, comorbidity clustering emerges from the interconnected nature of BH4-dependent pathways and the regulatory system domains.
The model proposes that activation of the BH4 Shunt produces coordinated changes across the Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation domains because these systems do not function independently.
As pathway activity shifts within one domain, downstream effects can propagate throughout the broader regulatory network.
The model proposes that this biological interconnectedness results in predictable patterns of co-occurring traits and conditions rather than isolated comorbidities.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
- Litman et al. (2025) . Princeton researchers demonstrated that phenotypic and clinical outcomes correspond to genetic and molecular programs of common, de novo, and inherited variation and further characterized distinct biological pathways disrupted by each class of mutation. These findings provide converging evidence that autism traits and comorbid traits may emerge through coordinated biological programs rather than isolated genetic effects, consistent with the model's prediction of comorbidity clustering across interconnected regulatory domains.
4. What Remains Untested
Research Gap
- Predictive clustering models.
- Cluster-specific interventions.
- Earlier identification of regulatory patterns before multiple comorbid traits emerge.
- Autism-comorbidity clustering observations.
- Symptom-specific treatment approaches.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Regulatory System Domains
1. Proposed Mechanism
Within the model, comorbid traits emerge when epigenetic redox-sensitive protein shunts alter pathway activity within specific regulatory system domains. These protein shunts function as regulatory system effectors that modify biological activity following a set-point breach.
Because each domain contains distinct pathways, proteins, and biological functions, activation of different protein shunts can produce different comorbid traits. The examples below highlight representative mechanisms that may contribute to trait development within each domain.
The model organizes these effects into five interconnected regulatory system domains: Immune System, Metabolism, Cellular Repair, Nervous System, and Genetic Regulation. While separated for clarity, these domains interact continuously and changes within one domain may propagate throughout the broader regulatory network.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
The remaining validation requirements have not yet been specified.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Immune System Domain
1. Proposed Mechanism
KEAP1 Shunt
The KEAP1 Shunt regulates antioxidant defense activation through the KEAP1-NRF2 pathway.
ROS-mediated activation shifts biological resources toward glutathione synthesis, detoxification, redox buffering, and damage control.
The model proposes that prolonged activation alters immune signaling, inflammatory regulation, and metabolic allocation, producing immune-related comorbid traits.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- Mechanism-specific interventions.
- Antioxidant response
- Inflammatory signaling
- Immune tolerance alterations
- No targeted interventions currently identified.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Metabolism Domain
1. Proposed Mechanism
MTR Shunt
The MTR Shunt regulates one-carbon metabolism and methylation capacity.
The model proposes that MTR activity reallocates methylfolate, cobalamin, homocysteine, and transsulfuration pathway resources according to metabolic demands.
These shifts alter methylation, glutathione production, energy metabolism, and redox regulation, producing metabolic comorbid traits.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- Direct BH4 Shunt interventions.
- Methylation changes
- Glutathione regulation
- Homocysteine metabolism
- Energy production
- Pathway-specific nutritional and metabolic interventions.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Cellular Repair Domain
1. Proposed Mechanism
MMP Shunt
The MMP Shunt regulates extracellular matrix turnover and remodeling.
The model proposes that altered MMP activity shifts cellular resources between tissue maintenance, repair, and degradation.
Persistent dysregulation may impair connective tissue integrity and structural maintenance, producing cellular repair-related comorbid traits.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- MMP-targeted therapies.
- hEDS
- Connective tissue dysfunction
- Extracellular matrix remodeling
- Chronic pain syndromes
- Primarily symptom-based management approaches.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Nervous System Domain
1. Proposed Mechanism
CRH Shunt
The CRH Shunt regulates neuroimmune stress signaling through activation of the hypothalamic-pituitary-adrenal (HPA) axis.
The model proposes that persistent CRH activation reallocates resources toward stress adaptation and alters immune, endocrine, and nervous system regulation, producing stress-related comorbid traits such as burnout.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- ACE2-targeted research.
- POTS
- Dysautonomia
- Sensory regulation
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.
Genetic Regulation Domain
1. Proposed Mechanism
p53 Shunt
The p53 Shunt regulates genomic preservation and damage control.
The model proposes that activation reallocates resources away from growth and specialization toward DNA repair, cell-cycle arrest, and cellular cleanup.
Chronic activation may reduce regenerative capacity and contribute to age-related or repair-related comorbid traits such as cancer.
2. Prior Converging Evidence
No dated pre-2023 study is currently listed for this node.
3. Outcomes of Studies Testing the Cascade’s Predicted Mechanisms
No dated study outcome testing the predicted mechanism is currently listed for this node.
4. What Remains Untested
Major Gap
- Protein-shunt-targeted interventions.
- DNA repair
- Cellular senescence
- Apoptosis
- Genomic stability
- Research remains focused on downstream pathway activity.
5. Falsifiability: What Could Disconfirm This Node
This node would be weakened or falsified if replicated evidence demonstrated one or more of the following:
- Biomarker evidence: The predicted dysregulation is not found in relevant biomarker datasets when the biological state represented by this node is measured.
- Trait classification: The traits associated with the measured biological dysregulation are not consistent with the Cascade's classification or predicted downstream effects. For an upstream node, this includes whether downstream resource allocation produces the predicted cluster of traits.
- Biological mechanism: The proposed protein function, pathway relationship, regulatory mechanism, feedback relationship, or biological effect of this node is demonstrated to be biologically inaccurate.
Individual variation in whether this node is involved, where it appears relative to an individual's most-upstream identifiable node, or how strongly it is expressed does not by itself falsify the node. Falsification requires evidence that the predicted biomarker state, trait classification, or biological mechanism fails when this node and its relevant biological context are directly tested.