Hypothesis: Immune Misattribution Theory (IMT): A Predictive Processing Framework for Allergic Disease in the Anthropocene Catarina Cunha, Ph.D.

Abstract

The prevalence of allergic diseases has increased dramatically across industrialized societies during the past century, yet no single explanatory framework has adequately accounted for the magnitude, timing, and breadth of this phenomenon. Existing theories—including the Hygiene Hypothesis, Old Friends Hypothesis, Microbiome Hypothesis, Environmental Pollution Hypothesis, and Epithelial Barrier Hypothesis—have each identified important contributors to allergic disease but have not converged on a unified mechanistic explanation. Here, we propose Immune Misattribution Theory (IMT), a framework suggesting that the central defect underlying modern allergic disease is not increased immune activity itself, but an increased probability that the immune system incorrectly assigns danger status to harmless antigens.

According to IMT, environmental pollutants, pesticides, microplastics, industrial chemicals, food additives, microbiome depletion, chronic psychosocial stress, and epithelial barrier dysfunction all act by increasing background danger signaling while simultaneously reducing the fidelity of immune threat discrimination. Under these conditions, harmless antigens repeatedly encountered during inflammatory states become misclassified as threats. Allergic disease, therefore, represents a failure of immune attribution rather than a simple excess of immune activation.

Drawing upon concepts from the Danger Model, trained immunity, psychoneuroimmunology, microbiome science, epithelial barrier biology, and predictive processing theories of cognition, we propose that allergic disease can be understood as a disorder of immune causal inference. Within this framework, immune memory represents stored probabilistic estimates of threat rather than simple records of exposure, and allergic sensitization emerges when repeated environmental co-occurrences produce persistent overestimation of antigen-associated danger.

Immune Misattribution Theory generates multiple experimentally testable predictions, including the existence of measurable immune attribution errors, additive effects of diverse environmental stressors on sensitization risk, and improved prediction of allergy development through composite measures of danger-signal burden rather than individual exposures alone. By reframing allergy as a failure of immune attribution rather than merely immune hyperreactivity, IMT offers a unified conceptual model linking environmental change, immune regulation, and the modern allergy epidemic.

Main Hypothesis Statement

Immune Misattribution Theory proposes that allergic diseases arise when the immune system repeatedly assigns danger status to harmless antigens because chronic environmental and physiological danger signals distort immune causal inference, resulting in persistent overestimation of antigen-associated threat.

Introduction

One of the most striking epidemiological developments of the modern era is the rapid increase in allergic disease. Rates of food allergy, asthma, eczema, allergic rhinitis, and autoimmune disorders have increased substantially throughout industrialized nations over the past fifty years. This increase has occurred too rapidly to be explained by genetic evolution alone and strongly suggests a major environmental component.¹–⁵

Several influential theories have attempted to explain this phenomenon. The Hygiene Hypothesis suggests that insufficient microbial exposure during childhood leads to improper immune education.¹ The Old Friends Hypothesis argues that loss of evolutionarily familiar microorganisms disrupts immune regulation.² The Microbiome Hypothesis emphasizes the role of microbial diversity in maintaining immune tolerance.⁶–⁸ The Epithelial Barrier Hypothesis proposes that environmental exposures damage epithelial surfaces, allowing inappropriate antigen penetration.⁹˒¹⁰ Environmental Pollution Models identify pollutants as drivers of inflammation and sensitization.¹¹–¹³

Each explanation appears partially correct.

Immune Misattribution Theory proposes that these observations represent different manifestations of a common underlying process.

Immune Prediction and Causal Inference

Recent developments in neuroscience suggest that the brain functions as a predictive system that continuously generates hypotheses about the causes of incoming sensory signals.¹⁴˒¹⁵ Under predictive processing frameworks, perception is fundamentally a process of inference.

The brain does not simply react to reality.

It continuously estimates:

What is most likely causing the signals I am receiving?

We propose that the immune system performs an analogous task. Rather than merely recognizing foreign molecules, the immune system continuously estimates what is most likely causing tissue damage or danger. This view is consistent with the Danger Model proposed by Matzinger, which argues that immune activation is organized around danger detection rather than simple recognition of foreignness.¹⁶

Trained Immunity

Recent discoveries demonstrate that innate immunity possesses memory-like properties.¹⁷–²⁰ Exposure to microbial products, environmental pollutants, and inflammatory stimuli can induce long-term epigenetic and metabolic reprogramming of innate immune cells.¹⁷˒¹⁸

Under IMT, trained immunity functions as a shift in baseline threat expectation. Chronic inflammatory exposures increase the prior probability assigned to danger, thereby increasing susceptibility to false-positive threat attribution.

The Attribution Problem

The fundamental task of the immune system is not to distinguish self from non-self.

Rather, the immune system must determine:

What is causing danger?

This problem resembles causal inference.

At every moment, the immune system encounters thousands of:

• Food proteins

• Microbial products

• Environmental chemicals

• Inhaled particles

• Cellular debris

• Metabolic byproducts

Most are harmless.

A successful immune system must correctly attribute danger signals to the true source of threat.

Immune Misattribution Theory proposes that allergic disease emerges when this attribution process fails.

Immune Prediction and Causal Inference

Recent developments in neuroscience suggest that the brain functions as a predictive system that continuously generates hypotheses about the causes of incoming sensory signals. Under predictive processing frameworks, perception is fundamentally a process of inference.

The brain does not simply react to reality.

It continuously estimates:

What is most likely causing the signals I am receiving?

We propose that the immune system performs an analogous task.

Rather than merely recognizing foreign molecules, the immune system continuously estimates:

What is most likely causing tissue damage or danger?

At any given moment, immune cells integrate information from:

• Food antigens

• Microbial products

• Tissue damage signals

• Environmental pollutants

• Microbiome-derived metabolites

• Hormonal signals

• Previous immune experience

These signals are inherently ambiguous.

The immune system must infer which environmental component represents the actual threat.

Under normal conditions, microbiome-derived tolerance signals, intact epithelial barriers, and regulatory immune pathways maintain attribution accuracy.

However, chronic exposure to pollutants, barrier dysfunction, microbiome depletion, and persistent inflammation may bias immune inference toward false-positive threat detection.

Allergic sensitization can therefore be interpreted as an error in biological causal inference.

The Bayesian Immune System

Formally, the immune system can be viewed as estimating:

P(Threat | Observations)

The probability that an antigen represents danger depends upon previous experience, current context, and background inflammatory state.

Repeated environmental exposures create situations where:

Food Antigen + Pollution + Barrier Damage + Inflammation

occur simultaneously.

The immune system repeatedly observes:

Food Antigen → Danger Context

As a consequence, Bayesian updating gradually increases:

P(Danger | Food Antigen)

Eventually, the food antigen alone becomes sufficient to trigger a danger prediction.

The allergy is established.

The Central Principle

The probability of allergy increases when:

Danger Signal Intensity increases

and

Threat Attribution Accuracy decreases

This creates conditions under which harmless antigens become statistically associated with inflammatory signals.

Repeated exposure eventually establishes:

• IgE production

• Mast cell priming

• Memory B-cell responses

• Antigen-specific inflammatory programs

The harmless antigen becomes permanently labeled as dangerous.

Sources of Misattribution

Environmental Pollutants

Air pollution, diesel exhaust particles, ozone, heavy metals, pesticides, herbicides, fungicides, industrial chemicals, food additives, and microplastics induce oxidative stress and inflammatory signaling.

These exposures increase background danger signals.

They do not necessarily become allergens themselves.

Instead, they increase attribution errors.

Microbiome Collapse

The microbiome acts as an information-processing system that teaches immune tolerance.

Loss of microbial diversity reduces the immune system's ability to recognize harmless antigens.

As tolerance signals decline, attribution accuracy deteriorates.

Microbiome depletion, therefore, increases susceptibility to immune misattribution.

Epithelial Barrier Dysfunction

Healthy epithelial barriers regulate antigen exposure and filter environmental information.

When barriers are disrupted:

• More antigens enter tissues

• More inflammatory signals occur simultaneously

• Signal-to-noise ratios decline

This increases opportunities for causal confusion.

Trained Immunity

Recent discoveries demonstrate that innate immunity possesses memory-like properties.

Repeated inflammatory exposures create a state of persistent immune vigilance through epigenetic and metabolic reprogramming.

Under IMT, trained immunity functions as a shift in baseline threat expectation.

The immune system becomes more likely to interpret ambiguous signals as dangerous.

Psychological Stress

Chronic stress activates neuroimmune pathways involving:

• Cortisol dysregulation

• Sympathetic activation

• Inflammatory cytokine production

Stress may therefore function as a non-chemical amplifier of misattribution risk.

A Learning Theory of Allergy

IMT suggests allergies resemble maladaptive learning.

The nervous system learns associations.

The immune system learns associations.

Repeated co-occurrence of:

Food Antigen + Danger Signal

may eventually produce:

Food Antigen → Danger Response

This parallels classical conditioning while remaining mechanistically distinct.

The theory therefore builds upon observations from psychoneuroimmunology and conditioned immune responses while extending them into a broader framework of immune causal learning.

Immune Memory as Stored Threat Probability

Traditional immunology describes memory B cells and memory T cells as storage systems for previous antigen encounters.

IMT extends this concept.

Immune memory may represent stored probabilistic estimates regarding threat.

Under this framework:

Immune Memory = Stored Threat Probability

Allergic memory therefore represents an incorrect belief regarding antigen danger.

The immune system is not remembering exposure alone.

It is remembering a threat estimate.

The Exposome and Attribution Overload

Humans evolved in environments characterized by:

• Natural microbial diversity

• Low levels of synthetic chemicals

• Minimal industrial pollution

Modern populations encounter tens of thousands of anthropogenic compounds throughout life.

The cumulative exposome may overwhelm evolved immune attribution mechanisms.

The result may be attribution overload.

Under such conditions, false-positive threat assignments become increasingly likely.

A Conceptual Mathematical Framework

The probability of allergy development may be approximated by:

Risk of Allergy ∝

(Danger Signal Load × Antigen Exposure × Immune Training)

÷

(Tolerance Signals × Barrier Integrity × Microbiome Diversity)

Where:

Danger Signal Load includes:

• Pollution

• Pesticides

• Microplastics

• Chronic inflammation

• Psychological stress

Tolerance Signals include:

• Regulatory T-cell activity

• Microbial metabolites

• Oral tolerance mechanisms

This equation is conceptual rather than quantitative but generates experimentally testable predictions.

Predictions

IMT generates several unique predictions.

Prediction 1

Allergy prevalence should correlate better with cumulative danger-signal burden than with any single environmental exposure.

Prediction 2

Multiple low-level stressors should be more allergenic than one strong exposure.

Prediction 3

Pollutants, microplastics, microbiome disruption, psychological stress, and epithelial damage should demonstrate additive effects.

Prediction 4

Individuals with superior immune regulatory function should resist misattribution despite exposure.

Prediction 5

Food allergy should develop preferentially toward antigens encountered during periods of elevated inflammatory signaling.

Prediction 6

Measures of trained immunity should predict future allergic sensitization before symptoms emerge.

Prediction 7

Restoration of microbiome diversity should improve attribution accuracy and reduce false-positive immune responses.

Proposed Experimental Tests

Experiment 1: Antigen Attribution Study

Groups:

A. Peanut alone

B. Peanut + pesticide

C. Peanut + particulate matter

D. Peanut + microbiome depletion

E. Peanut + combined exposures

Prediction:

Group E develops the strongest allergic sensitization.

Experiment 2: Timing Experiment

Expose identical food antigens:

• During inflammation

• During non-inflammatory periods

Prediction:

Sensitization occurs preferentially during inflammatory windows.

Experiment 3: Human Prospective Cohort

Measure:

• Exposome burden

• Microbiome diversity

• Barrier integrity

• Stress biomarkers

• Allergy outcomes

Prediction:

A composite Immune Misattribution Risk Score predicts allergy better than any individual factor.

Experiment 4: Attribution Specificity Test

Pair Food A repeatedly with inflammatory stimuli.

Present Food B without inflammatory stimuli.

Prediction:

Allergy develops preferentially toward Food A.

This experiment directly tests immune misattribution.

Experiment 5: Immune Prediction Error Study

Introduce novel food antigens during controlled inflammatory and non-inflammatory states.

Measure:

• IgE production

• Regulatory T-cell responses

• Trained immunity markers

Prediction:

Greater immune prediction error leads to increased sensitization.

Figure 1: Conceptual Model

Environmental Exposome

↓

Pollution + Microbiome Loss + Barrier Dysfunction + Stress

↓

Increased Danger Signaling

↓

Reduced Attribution Accuracy

↓

Immune Misattribution

↓

Allergic Memory Formation

↓

Clinical Allergy

Implications

If IMT is correct, modern allergic disease is fundamentally an information-processing disorder.

The immune system is not merely overactive.

It is making incorrect causal inferences.

The modern environment may overwhelm ancient immune attribution mechanisms through unprecedented combinations of pollutants, microbiome disruption, chronic stress, barrier dysfunction, and persistent low-grade inflammation.

The result is not simply more immunity.

The result is immunity directed at the wrong targets.

Limitations and Alternative Explanations

Immune Misattribution Theory is intended as a conceptual framework rather than a proven mechanism.

Several limitations must be acknowledged.

First, direct evidence that immune systems perform Bayesian-style causal inference remains limited. Predictive processing is employed as an explanatory framework rather than a literal computational claim.

Second, many observations explained by IMT can also be interpreted through existing frameworks including the Hygiene Hypothesis, trained immunity, and the Epithelial Barrier Hypothesis.

Third, allergic disease is heterogeneous and influenced by genetic predisposition, developmental factors, socioeconomic variables, and dietary patterns.

Finally, it remains possible that allergic disease results from multiple independent mechanisms rather than a single underlying process.

The strongest test of IMT will therefore be its ability to generate unique and falsifiable predictions.

Conclusion

Immune Misattribution Theory provides a unified framework linking trained immunity, microbiome depletion, epithelial barrier dysfunction, environmental pollution, psychoneuroimmunology, predictive processing, and associative immune learning.

The theory proposes that allergic disease emerges when harmless antigens become incorrectly assigned danger status during periods of chronic inflammatory signaling.

By reframing allergy as a disorder of immune attribution rather than merely immune activation, IMT offers a new conceptual foundation for understanding the modern allergy epidemic and generates a broad range of experimentally testable predictions.

If validated, IMT would suggest that the central challenge of immune health is not merely suppressing inflammation but improving the accuracy with which the immune system identifies the true causes of danger. 

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