Periodic Reporting for period 1 - PredicGenX (Responsible prediction of gene expression: mitigating genetic risk profiling)
Reporting period: 2023-05-01 to 2025-08-31
Environmental factors are crucial to physical and mental health—they impact even the expression of our DNA. The study of epigenetics provides better understanding of gene-environment interaction. Environmental influence can come from outside and inside the organism but one environmental factor that is typically overlooked is people’s knowledge production and beliefs. PredicGenX responds to the scientific worry that predictions about genes are likely to be reflexive i.e. they impact the eventual outcome. Scholars have raised concerns that beliefs about genetic information affects genetic risk to match that information—a so-called self-fulfilling prophecy not unlike the placebo and nocebo effects. Studies showed that receiving one’s genetic risk profile can change physiology independent of actual genetic risk. Moreover, the current trend to focus on risk, biomarkers, and early diagnostics produces 'knowledge' which is inevitably based on undetermined information, given that gene expression is not fixed. Asserting genetic information as determined when the assertion itself has potential impact on genetic expression is especially alarming considering the popularity of direct-to-consumer genetic testing and precision medicine. Understanding the direct impact of genetic predictions on gene-expression, its vulnerability to feedback loops, and their moral implications is crucial, urgent, yet currently lacking. With this project, I aim to fill that knowledge gap. Through qualitative fieldwork and philosophical analysis, I will theorise the different ways in which reflexive (epi)genetic prediction manifests and—while detailing a descriptive account of the different models—offer an analysis of the epistemic and ethical implications of their reflexivity on research and practice, and the meaning for therapeutic intervention.
Reflexive prediction in (epi)genetics:
Collaboration with clinical geneticist Angus Clark. Angus works both as a professor in the Cardiff University's Division of Cancer & Genetics (50%) and as (honorary) consultant in the All Wales Medical Genomics Service (50%). Angus' empirical observations of the ways in which genetic predictions about health impacted health outcomes matched the ways in which Mayli described (potential) reflexive predictions in genetics and medicine, more broadly. A first article is describing the more straightforward reflexivity between predictions of health and health outcomes through the conscious and/or explicit responses to the predictions. A second article will go deeper into the not so obvious, more covert, psychological and/or subconscious responses that influence health outcomes.
Reflexive prediction in medicine, more broadly:
Mayli's comprehensive theory for analysis of all self-fulfilling prophecies allows anyone to examine the 4 conditions required for any reflexivity and for self-fulfilling reflexivity in particular. Central to each investigation is the identification and examination of the "employment sensitivity" in any particular case. Understanding employment sensitivity in a given case opens up the potential for controlling and shaping any kind of reflexivity. A first publication describes the full fledged theory of self-fulfilling prophecy in medicine and biology. A second article will mirror this theory and analysis from the self-defeating side of the reflexive spectrum. A final article compares the two and brings the full spectrum into vision.
The rise of AI has brought the use of automated prediction in clinical research and practice to the next level. Yet, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. With a research team from Duke-NUS Medical School in Singapore, specialised in Resuscitation science, Mayli wrote an overview that aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research.
Predictions are typically made in response to uncertainty, in order to inform the course of action. However, there are two kinds of uncertainty in medicine that are often confounded into a general medical uncertainty. In order to facilitate analysis of reflexive predictions, Mayli offers conceptual clarity between physiological and normative uncertainty. Whereas the former invites predictions about the feasibility of treatment to reach a specific treatment goal, the latter invites predictions about whether a specific treatment goal is desirable. The practical relevance and implementation of this conceptual work is showcased in the research collaboration on fertility outcome measures.
Collaboration on fertility outcome measures with Heidi Mertes, associate professor in Medical Ethics at Ghent University and one of the founding members of both the METAMEDICA consortium and of the Bioethics Institute Ghent. Heidi's empirical findings from her research on the ethics of medically assisted reproduction, embryo research, genetic parenthood, fertility education, and disruptive innovation in healthcare matched Mayli's reflexive theory of how conceptual predictions can be self-fulfilling in both normative and physiological ways. Their article first describes precisely in what ways fertility outcome measure become interpretative self-fulfilling prophecies: those who achieve the goal consider themselves successful and those who do not consider themselves failures. Second, it offers an alternative fertility outcome measure which can be equally self-fulfilling, according to which a successful treatment is one in which people leave the clinic released from the suffering that accompanied their status as infertile when they first entered the clinic. This new outcome measure still implies that walking out with a healthy baby is a positive outcome. What changes is that walking out without a baby can also be a positive outcome, rather than being marked exclusively as a failure.
Collaboration with clinical geneticist Angus Clark. Angus works both as a professor in the Cardiff University's Division of Cancer & Genetics (50%) and as (honorary) consultant in the All Wales Medical Genomics Service (50%). Angus' empirical observations of the ways in which genetic predictions about health impacted health outcomes matched the ways in which Mayli described (potential) reflexive predictions in genetics and medicine, more broadly. A first article is describing the more straightforward reflexivity between predictions of health and health outcomes through the conscious and/or explicit responses to the predictions. A second article will go deeper into the not so obvious, more covert, psychological and/or subconscious responses that influence health outcomes.
Reflexive prediction in medicine, more broadly:
Mayli's comprehensive theory for analysis of all self-fulfilling prophecies allows anyone to examine the 4 conditions required for any reflexivity and for self-fulfilling reflexivity in particular. Central to each investigation is the identification and examination of the "employment sensitivity" in any particular case. Understanding employment sensitivity in a given case opens up the potential for controlling and shaping any kind of reflexivity. A first publication describes the full fledged theory of self-fulfilling prophecy in medicine and biology. A second article will mirror this theory and analysis from the self-defeating side of the reflexive spectrum. A final article compares the two and brings the full spectrum into vision.
The rise of AI has brought the use of automated prediction in clinical research and practice to the next level. Yet, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. With a research team from Duke-NUS Medical School in Singapore, specialised in Resuscitation science, Mayli wrote an overview that aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research.
Predictions are typically made in response to uncertainty, in order to inform the course of action. However, there are two kinds of uncertainty in medicine that are often confounded into a general medical uncertainty. In order to facilitate analysis of reflexive predictions, Mayli offers conceptual clarity between physiological and normative uncertainty. Whereas the former invites predictions about the feasibility of treatment to reach a specific treatment goal, the latter invites predictions about whether a specific treatment goal is desirable. The practical relevance and implementation of this conceptual work is showcased in the research collaboration on fertility outcome measures.
Collaboration on fertility outcome measures with Heidi Mertes, associate professor in Medical Ethics at Ghent University and one of the founding members of both the METAMEDICA consortium and of the Bioethics Institute Ghent. Heidi's empirical findings from her research on the ethics of medically assisted reproduction, embryo research, genetic parenthood, fertility education, and disruptive innovation in healthcare matched Mayli's reflexive theory of how conceptual predictions can be self-fulfilling in both normative and physiological ways. Their article first describes precisely in what ways fertility outcome measure become interpretative self-fulfilling prophecies: those who achieve the goal consider themselves successful and those who do not consider themselves failures. Second, it offers an alternative fertility outcome measure which can be equally self-fulfilling, according to which a successful treatment is one in which people leave the clinic released from the suffering that accompanied their status as infertile when they first entered the clinic. This new outcome measure still implies that walking out with a healthy baby is a positive outcome. What changes is that walking out without a baby can also be a positive outcome, rather than being marked exclusively as a failure.
The investigation into the not so obvious, more covert, psychological and/or subconscious responses to genetic prediction that influence health outcomes leads to scientifically novel (and, to some, surprising) root causes that are demonstrated in groundbreaking empirical research in the fields of developmental biology, regenerative medicine, and quantum physics. Rather than continuing to look for biochemical explanations, researchers at the Tufts Center for Regenerative and Developmental Biology are investigating and demonstrating bioelectric explanations which I deem of crucial importance. Under the leadership of evolutionary biologist and computer scientist Michael Levin, we are seeing the potential impacts of such understanding and the results of reflexivity when radically new perspectives serve as new base assumptions in the fields of biology, genetics, and medicine.