Does the word Pathogenesis belong in discussions of autism?
In a recent study published in the journal Nature Neuroscience, researchers present a framework to utilize “multi-omic data from well-defined groups and assess the contribution of gut-brain axis (GBA) disruptions in the pathogenesis of autism spectrum disorder (ASD).” Today, I want to break down this study and shed some light on why there always seems to be enough money for such work, but never enough to support autistic people in living a safe, comfortable life. Buckle up. This is going to be a long info-dump. But, it’s important because this is what the system is spending it’s money on. It’s the latest shiny object.
First of all, some definitions (omics)
“Multi-omic data” refers to the integration and analysis of multiple types of biological data sets derived from various "omics" technologies. Omics refers to the study of different aspects of biological molecules and their interactions, such as genomics (study of the genome), transcriptomics (study of gene expression), proteomics (study of proteins), metabolomics (study of metabolites), and epigenomics (study of epigenetic modifications).
Each “omics technology” provides a unique perspective on the biological system, and by combining multiple omics data sets, researchers can gain a more comprehensive understanding of complex biological processes and systems. Multi-omic data integration is supposed to allow for a “holistic analysis of molecular interactions and networks,” enabling researchers to identify patterns, correlations, and relationships that may not be apparent when analyzing individual omics data sets in isolation.
For example, by integrating genomics, transcriptomics, and proteomics data, researchers can study how genetic variations affect gene expression and protein levels, providing insights into the functional consequences of genetic mutations. Similarly, integrating transcriptomics and metabolomics data can shed light on how gene expression profiles relate to metabolic pathways and identify potential biomarkers for diseases.
Analyzing multi-omic data sets often involves sophisticated computational and statistical techniques to extract meaningful information and uncover underlying biological mechanisms. The integration of multi-omic data has the potential to revolutionize our understanding of complex biological systems, disease mechanisms, and personalized medicine, leading to improved diagnostics, therapeutic strategies, and precision treatments.
First Questions: reliability and validity
The reliability of multi-omic data analyses depends on several factors, including the quality of the data, the analytical methods employed, and the interpretation of the results. Whilst multi-omic approaches have the potential to provide comprehensive insights into biological systems, it is important to consider certain considerations:
Data Quality: The reliability of any analysis depends on the quality of the underlying data. Issues such as noise, biases, batch effects, and technical variations can affect the accuracy and reproducibility of multi-omic data. It is essential to ensure robust data collection and quality control measures to minimize these potential sources of error.
Analytical Methods: The choice of analytical methods and algorithms plays a critical role in the reliability of multi-omic data analyses. The complexity and interconnectedness of omics data require advanced computational approaches, such as machine learning, network analysis, and statistical modeling. The appropriate selection and validation of these methods are crucial to obtaining reliable and meaningful results.
Integration Challenges: Integrating multiple omics data sets can be challenging due to differences in data types, scales, and platforms. The integration process requires careful consideration of normalization, data transformation, and statistical integration methods. It is important to validate the integration approach and assess its impact on the final results.
Biological Interpretation: The interpretation of multi-omic data analyses requires biological expertise and domain knowledge. The integration of diverse data sets may generate complex and extensive datasets, making it essential to identify meaningful patterns and validate findings through experimental validation or comparison with existing biological knowledge.
Reproducibility and Validation: Reproducibility is a critical aspect of any scientific analysis. It is important to document and share the analytical pipelines, software tools, and parameters used in multi-omic data analyses. Validation of findings using independent datasets or experimental validation further strengthens the reliability of the results.
Next definition: pathogenesis
Pathogenesis refers to the process or mechanism by which a disease or disorder develops within an organism. It involves the study of the origin, development, and progression of a disease, including the cellular and molecular events that occur during the course of the disease.
Pathogenesis encompasses various factors and mechanisms that contribute to the development of a disease. These may include “genetic predisposition, environmental factors, infectious agents (such as bacteria, viruses, or parasites), immune system dysfunction, metabolic imbalances, or a combination of these factors.”
Understanding the pathogenesis of a disease is crucial for “diagnosing and treating it effectively.” It involves studying the interactions between the pathogen or disease-causing agent and the host organism, as well as the underlying biological processes that lead to the manifestation of clinical symptoms.
By unraveling the pathogenesis of a disease, researchers can identify potential targets for intervention and develop strategies for prevention, early detection, and treatment. This knowledge also helps in the development of vaccines, drugs, and therapies that specifically target the mechanisms involved in the progression of the disease.
Did you catch all of that?
In researching the pathogenesis of autism, researchers hope to discover a proper intervention to prevent it from happening. This may be a vaccine, drug(s), or therapy. If an autistic person happens to slip past this regime, then “science” can come to the rescue with drugs and therapies to “treat” our autism. That’s quite a mouthful.
Do you trust these researchers yet?
Concluding the last paragraph of the introductory remarks of the paper comes this admission:
Ultimately, our analysis highlights the inherent limitations of cross-sectional studies for understanding the dynamics of the functional architecture of autism and provides a framework for future studies aimed at better defining the causal relationship between the microbiome and other omic levels and ASD.
Do we need to go on and read the rest of the paper after this admission? Sadly, yes. We need to know what they’re after, how they ultimately plan to rid the world of us. If you’re a paid subscriber, keep reading. If not, I encourage you to support my work with a paid subscription.
Methods and Practices
The researchers hope to mine this different data sets using a “Bayesian differential ranking algorithm.” That sounds important and complicated. Here’s what that it means, putting my PhD hat on for a moment.
A Bayesian differential ranking algorithm is a statistical method used for comparing and ranking multiple items or entities based on observed data. It incorporates Bayesian inference principles to estimate the probabilities or ranks of different items in a dataset, taking into account prior knowledge and updating it with observed evidence.
The algorithm is typically applied in scenarios where there are multiple entities or treatments, and the goal is to determine their relative performance or effectiveness. It is commonly used in fields such as bioinformatics, genomics, and drug discovery to compare gene expression profiles, identify differentially expressed genes, or rank the efficacy of different drugs or treatments.
The Bayesian differential ranking algorithm starts with prior beliefs or assumptions about the ranking of the entities based on available knowledge or prior studies. As new data is collected, the algorithm updates the rankings by combining the prior beliefs with the observed evidence using Bayesian principles. This process involves calculating the posterior probabilities or ranks of each entity based on the observed data, taking into account the uncertainty and variability of the measurements.
The algorithm can handle various types of data, such as continuous measurements, binary outcomes, or count data, depending on the specific application. It also allows for the incorporation of covariates or factors that may influence the rankings, enabling the adjustment for confounding variables.
By leveraging Bayesian inference, the algorithm provides a principled and flexible framework for ranking and comparing entities, accounting for uncertainty and prior knowledge. It can be a valuable tool for decision-making, identifying top-ranking entities, and guiding further investigations or interventions based on the obtained rankings.
With this methodology, the researchers hope to overcome the inherent limitations of traditional meta-analyses by developing their own approach for controlling for select confounders that they hope would help reveal a comprehensive picture of autism-specific “omic signals.” In other words, are there patterns in the data that can be used to detect the presence of the autistic neurotype? If so, how can it be easily operationalized, and thus weaponized against autistic people?
Conclusions
This study continued the largely problematic gut biome cause of autism theory, or the gut-brain axis disruptions that may cause autism. The study’s findings provide future researchers a framework to define / refine what the authors believe may be the causal relationships between the microbiome, other omic levels, and autism. They simply won’t stop looking for the “root cause” of the autistic neurotype.
Nevertheless, research money continues to flow to such studies. Once the algorithms find the genetic or omic descriptor of autism, do you think there will be an in-vitro test for autism? If so, what do you think doctors will advise if the test turns up positive?
Why does it seem that there’s no place for autism in this world? Why, again, does society spend so much trying to find a cure / prevention / test and so little to help us simply get on in life?
The concept of “curing” autism is of course complex and controversial, as autism is not a disease to be eradicated. It is a neurological difference that is a natural part of the human experience. It has been with humanity since before time was time. It is also integral to an individual's identity and neuro-developmental profile.
The idea of “curing” autism implies that there would eventually be a medical intervention or treatment that would eliminate or significantly reduce the core characteristics of autism. However, it is essential to consider the following aspects and potential implications:
Neurodiversity: Autism is considered part of the broader concept of neurodiversity, which recognizes and values the natural variation in human neurodevelopment. Many individuals and advocates within the autism community argue against the notion of a “cure” and emphasize acceptance, support, and accommodations for autistic individuals, promoting the idea that autism, of course, is a valid and valuable way of being.
Heterogeneity of Autism: Autism is a spectrum, meaning that the neurotype manifests differently in each individual. We humans each have a wide range of abilities, strengths, challenges, and characteristics. A potential “cure” would need to account for this inherent diversity and address the unique needs of each individual. It would also have to deal with the consequences of eradicating this neurotype - given that it has survived natural selection precisely because it’s important to humanity’s survival on this spinning rock.
Ethical Considerations: The pursuit of a “cure” for autism raises ethical considerations (duh!). It is important to respect the autonomy and agency of all humans (Dr. Mengele) and consider our perspectives and preferences. The desire for a “cure” should not undermine the acceptance and inclusion of individuals with autism within society. But, somehow it always does.
Focus on Support and Accommodations: Rather than seeking a “cure,” most of us advocates argue for focusing efforts on providing appropriate support and accommodations. Our approach aims to address specific challenges, improve quality of life, promote inclusion, and empower individuals to reach their full potential.
In summary, the concept of “curing” autism raises a host complex questions and considerations. Whilst efforts and funds should be directed towards supporting autistic individuals and their families, it is equally important to respect and value the diversity of neuro-developmental profiles and embrace the principles of acceptance, inclusion, and neurodiversity.