As we make progress in science, are we moving forward in developing unbiased research?
Did you know, humans are 99% to 99.9% similar in genetic makeup? A mere 0.1% could differentiate you from someone else, be it your round, button nose to the lankiness of your limbs, the way you store fat, or how fast your body metabolises ibuprofen. Even identical twins aren’t that similar due to genetic mutations that occur after fertilisation!
Who knew that a mere 0.1% difference would create an ‘us’ that’s so uniquely different, sufficient to categorise us into our biological sexes and races. But do our differences really matter? While in research, they act as variables that may affect research outcomes, the question is: when is it relevant and when is it purely biased?
Our evolutionary tale truly began when the Homo erectus, the oldest known early humans possessing modern human-like body proportions and originating in Africa approximately 1.78 million years ago, migrated out of Africa to Eurasia, forming the Zhoukoudian Peking man of northern China and the Java man in Indonesia, before returning to Africa as a completely new species — the Homo sapiens, the scientific term for us modern humans. Research shows that we evolved to have comparatively larger brain sizes than the Homo erectus, among other features.
But what caused these changes?
In an excerpt of his diary, Charles Darwin wrote, “...existing animals have a close relation in form with extinct species”. Putting it simply, he conceptualised the birth of a new species as an after-effect of environmental pressures, such as climate, food sources, and environmental threats, thus forming the basis of evolution. Essentially equating evolution with the natural process of ensuring a particular species survives as time progresses.
He was well aware that a diversity in physical features enabled the survival of a species through these selective pressures. What he missed was that the underlying basis of this physical variation arose from the microscopic workings of the genome; a long string of genetic codes that give every life on earth variations in their forms and function.
Like with any other animal, sexual reproduction, migrations, and genetic mutations create genetic diversity, enabling ancestral species to adapt to the selection pressures that’d have otherwise wiped them out if they were similar in genetic makeup. Even in the Himalayas, you’d see, spanning across the Tibetan Plateau and the Indian subcontinent, the genetic diversity is moulded within the Nepalese subgroups of Rai, Magar, Tamang, and Sherpa. Each cohort, genetically distinct from the other yet shares the same Tibetan ancestry at varying degrees.
The use of race as a research variable is deeply rooted within the longstanding belief that race correlates to genetic similarities. But in actuality, the ways in which we differ have little to do with our ideas of race and more so with our genetic history. The misuse of race, ethnicity, and sex as population descriptors in genomic research creates a distortion of how it can be attributed to our health and a subsequent misapplication in clinical settings. It’s therefore crucial to understand the instances where data is misused and, when used appropriately, the context of its causality.
Did you know that Africans are more genetically similar to Eurasians than to individuals from the same race?
This is the work of single nucleotide polymorphisms (SNPs), a form of genetic mutation. SNPs are the single letter modifications in the genetic code combinations, comprising over 90% of the 0.1% genetic difference among us. Not only does it make us different from people of the same race, it also accounts for our racial features.
Scientists discovered that geographical origins are closely related to their genetic origins or genetic ancestry, and that categorising people by identifying their SNPs is more accurate than relying solely on racial categories. Genetic ancestry plays an important role in research as it can elucidate health issues like side effects of drugs experienced by people of particular genetic ancestries.
On the other hand, though ethnicity is closely related to race, it’s not decoded by genetics but presented through epigenetic changes; a mechanism that changes the way genes are activated and contributes to health disparities, such as cancer, within those of the same ethnicity.
It accounts for certain gaps in research that genetic ancestry cannot explain as it considers the ethnic group’s shared norms, environments, and experiences as triggers for these changes. For example, African Americans are more likely to have worse health outcomes than white Americans because of shared experiences of childhood crime, discrimination, racism, and lower socioeconomic statuses (SESs).
Reporting race or ethnic-related health risks without context or deeper insights endangers us as it paints a deceptive representation that one’s potentially at lower risk of certain health issues compared to others. For instance, according to the 2019 National Health and Morbidity Survey (NHMS), the prevalence of obesity was highest among Indians. A study utilising the NHMS data described the use of BMI in its analysis and, in revealing that the ethnic Chinese had lower prevalence of obesity though likely to have normal-weight obesity (NWO), fosters the underdetection of cardiovascular disease risks, especially when ischemic heart disease is reported as the primary death among them.
Undisputedly, context is important behind scientific findings, integrating not only genetic ancestry but an individual’s social, behavioral, and environmental influences. This can be seen as the polynesian ancestry of native Hawaiians, though shown to positively correlate with obesity, the western diet was its causation after adapting it to their traditional diet.
Neurosexism, the myth that women and men have different brains, still runs blatant and rampant within STEM fields although sex-related cognitive abilities proved to be the unicorn of modern time — nonexistent. There’s a prevalent gender gap where women are sidelined even as a variable, demonstrated by their predisposition to adverse effects of 86 different, federally-approved medications due to a gender gap in US clinical trials.
The difference between sex and gender is progressively overt as scientists see the importance of sex and gender-based research. With a mere 200 genes, the Y chromosome that denotes the biological sex of a person, generated a drastic contrast between male and female constitutions. When you look at a person whose outer appearance appears to be those associated with a ‘man’, a few assumptions of his biological sex such as his ‘manliness’ and amount of testosterone comes into mind.
These assumptions, however, have very little to do with the ‘amount of testosterone’ but are associated with the response towards perceived threats to their masculinity. Psychologists in the 50s and 60s have since underlined the social, behavioral, environmental, and cultural aspects that make up a person’s ‘gender’, where society dictated it relative to biological sex. Although they seem interchangeable, it’s important to distinguish one from the other.
Many scientists assume that health-disparities naturally exist due to sex differences between men and women, but that isn’t quite so. Did you know, females are overall less likely to develop cancer due to tumor-suppressive genes on their pair of X chromosomes as compared to males with only one X chromosome?
But to some extent, the multifaceted aspects of gender contributed to males engaging in cancer risk behaviours such as higher rates of tobacco and alcohol consumption. With both genetic and sociobehavioural aspects raising cancer likelihoods, it’s no wonder there's a cancer sex bias inclined towards men.
While hormones and SNPs from sex chromosomes shape the prevalence of obesity in women, gender-disparities in obesity have started to grow more conspicuous with gender-based trends varying across countries. Even so, the distribution of obesity has been found useful in measuring gender equality with it being linked to discriminations in various aspects of economic, education, and health.
The granularity of Malaysian research must steer its focus beyond race, ethnicity, and sex. It’s pivotal for the inclusion of social, economical, health, and political aspects in population studies to provide sufficient context behind stereotypic causality.
Justifications should be included when racial or ethnic aspects are deemed relevant in research papers. An understanding of the social underpinnings of gender should go alongside sex as research variables.
As much as a 0.1% difference differentiates us, it’s also enough to segregate us. The misuse of data is clearly perpetrated by a severe lack of it. A trend for social disparity drives a tendency for misrepresentation while perpetuating lack of open and transparent dialogue.
If these misrepresentations continue, we may never progress beyond this social pandemic.
To fight, it goes back to asking this simple question: Is this relevant or is this biasness?
Similar to data bias in machine learning, data sets used to train algorithms are often finite and ill-reflect the population, potentially promoting confirmation bias of the groups at the short end of the stick. Scientists should always call to mind the universal value of objectivity in the scientific ethos as a measure against the spillover of biases into research.
Because in a world that’s increasingly outspoken on social disparities, to come in last in this race against inequality is to be left behind in progression.
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