The digital epidemiology of phenylketonuria, aka folling’s disease: retrospective analysis and geographic mapping via google trends

摘要

Background: Phenylketonuria, commonly known as PKU, is an inherited disorder in which there is an abnormally elevated blood level of the amino acid phenylalanine leading to several pathologies affecting multiple organs including the central nervous system and resulting in debilitating intellectual disability and other neuropsychiatric disorders. Phenylalanine is a building block of several critical proteins within the biological systems. Aims and Objective: To assess the digital epidemiology and geographic mapping of Phenylketonuria. Materials and Methods: This study is a retrospective analytic (2013?2017) of a very large database existing on the surface web known as Google Trends. it aims to extrapolate a statistical inference concerning the digital epidemiology and the geographic mapping of phenylketonuria. The trends database will be explored via thematic keywords specific to the condition of phenylketonuria including “Phenylketonuria [PKU]”, “Phenylalanine”, “Inborn errors of metabolism”, “Tetrahydrobiopterin”, and “Chromosome 12 (human)”. Results: The digital epidemiology is densely clustered in countries from the developed world, eastern Europe, and Latin America. Surface web users from China appears to possess the highest interest in phenylketonuria. The contribution of the Middle Eastern and Arabic countries to the geographic mapping did not exceed 10.51% at its best. Significant changes existed for year-to-year variations of trends. Statistical outliers were also found, the strongest of which was observed during April 2016 for which there’s no plausible explanation. Conclusion: Trends databases operating on the surface web represent potent tools of big data that can be exploited to assess the digital epidemiology and geographic mapping of countless phenomenon including rare genetic diseases and inborn errors of metabolism. There are also enormous potentials for real-time and predictive analytics of these databases when investing the application of automation in data collection and principles of machine learning. Asian Journal of Medical Sciences Vol.9(6) 2018 93-99.