Researchers have been looking for accurate methods to diagnose people with ASD for decades now. Despite there being plenty of techniques – including blood testing and genetic tests – still, none of them are considered to be reliable enough to use extensively. After having studied the scholarly resources that I have picked for my literature review, I can denote some of the reasons why it is so. First of all, it is a fact that the differences that are found in autistic children as compared to neurotypical children might not necessarily be related to ASD. There seems to be a variety of other factors that these can be attributed to, so one must be very careful in drawing definite conclusions. Moreover, researchers themselves acknowledge certain limitations in terms of sample groups, which negatively impact the legitimacy of the results even if a study shows particular patterns that one thinks should be considered. Finally, all of the studies recognize that the relevancy of their findings is to be confirmed by the findings of related studies; no independent research on such a complex subject can be deemed competent enough.
For example, Barone et al. (2018) explicitly state that they are not certain whether distinct metabolite differences – found in autistic children and used as a research subject – are actually related to ASD. According to them, these could be a product of a co-morbid undiagnosed medical condition or a vitamin D deficiency, often observed in children with ASD (Barone et al., 2018). Qin et al. (2018) come to the same conclusion regarding their study: they investigated that autistic children had higher levels of concentration of harmful metals in their blood than their neurotypical folks. However, there are several other factors it could be related to and, therefore, these metals cannot be fully confidently considered risk factors for ASD (Qin et al., 2018). In terms of sample groups, the lack of diversity is concerning: research subjects of Barone et al. (2018) were exclusively Caucasian, whereas Qin et al. (2018), although expectedly, studied Chinese kids only; the authors of both these studies as well as Rubenstein and Chawla (2018) admit that their studies’ sample types do not allow for more convincing results.
However, sample groups of other studies were large and diverse enough for their conclusions to be deemed relevant in that regard; this is where the papers differ. The one thing that all of them have in common, though, is their emphasis on the necessity of an integrated approach. Genovese and Butler (2020) note that advances in genomic testing, bioinformatics approaches, and computational predictions must all be combined to help recognize possible distinctive patterns of ASD in the future. Roberts et al. (2020) speak about how several various ASD-specific measures implemented by them in their study are the reason why they are so confident in the legitimacy of their outcomes. Barone et al. (2018), Qin et al. (2018), and Rubenstein and Chawla (2018) all echo the same thing in slightly different words: when it comes to both attempting to diagnose and treat the autistic spectrum disorder, comprehensiveness is key. It might be considered the main theme that appears in all the works and unites them together, despite the papers using different methods and approaches.
Seeing how complex the subject of ASD diagnosing is, several conclusions are to be drawn. First of all, specific conditions which might be prevalent in children with ASD are not to be deemed its cause. Therefore, ASD should not be automatically attributed to children who have these specific conditions, unfairly stereotyping them. As stated in the Bible, one must be careful when speculating: “Do not judge by appearances, but judge with right judgment” (John 7:24, n.d.). Said right judgment in the case of ASD belongs to the scientists and researchers – and even they cannot state anything definitively yet. Additionally, sample groups are to be taken into consideration when speaking about the research results. What works for a white male portion of the population from economically developed areas seldom does for women of color from poor countries – most likely, methods of determining complex disorders such as ASD included. Additionally, if a researcher wants their study on the most accurate methods of ASD diagnosing to be as legitimate as possible, they need to be prepared for long and hard work.
Work is to be long and hard due to the above-mentioned necessity to take a lot of things into consideration but, most prominently, expand the boundaries. Expanding the boundaries means attempting to make use of the findings from other fields on the same subject, increasing sampling groups and testing time, and being extremely careful in concluding. I would say that these three are the main approaches each researcher studying such a multifaceted issue as ASD diagnosing must implement. Granted, many of them already do – but if all of them are to resort to these, possibly, the answer will be found sooner than we expect.
References
Barone, R., Alaimo, S., Messina, M., Pulvirenti, A., Bastin, J., Ferro, A., Frye, R. E., & Tabbì, G. (2018). A subset of patients with autism spectrum disorders show a distinctive metabolic profile by dried blood spot analyses.Frontiers in Psychiatry, 9(636), 1-11. Web.
Genovese, A., & Butler, M. G. (2020). Clinical assessment, genetics, and treatment approaches in autism spectrum disorder (ASD).International Journal of Molecular Sciences, 21(13), 4726. Web.
John 7:24. (n.d.). Bible Gateway. Web.
Qin, Y. Y., Jian, B., Wu, C., Jiang, C. Z., Kang, Y., Zhou, J. X., Yang, F., & Liang, Y. (2018). A comparison of blood metal levels in autism spectrum disorder and unaffected children in Shenzhen of China and factors involved in bioaccumulation of metals. Environmental Science and Pollution Research, 25(18), 17950-17956. Web.
Roberts, J. E., Bradshaw, J., Will, E., Hogan, A. L., McQuillin, S., & Hills, K. (2020). Emergence and rate of autism in fragile X syndrome across the first years of life.Development and Psychopathology, 32(4), 1335-1352. Web.
Rubenstein, E., & Chawla, D. (2018). Broader autism phenotype in parents of children with autism: A systematic review of percentage estimates.Journal of Child and Family Studies, 27(6), 1705-1720. Web.