Identifying Causes of Breast Cancer and Autism: A Novel Method for Analyzing Non-infectious Disease Factors - Alan Olan (Disease and Health: Research Developments Vol. 1, 2024)
Background: Breast Cancer is a type of cancer that starts in the breast. Breast cancer occurs mostly in women, but men can develop breast cancer too. Currently, breast cancer is the second leading cause of cancer death in women in the USA. Breast cancer accounts for about 30% of all new female cancers each year. In the USA, there is a 1 in 8 chance for women to develop Breast Cancer. The exact causes of breast cancer are currently unknown. Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction and the presence of restricted, repetitive patterns of behaviors, interests, or activities that can cause an array of difficulties in social interaction, communication, and participation in daily activities. Autistic children often don’t speak, have delayed speech, or lose previous ability to say words or sentences. According to the USA Centers for Disease Control and Prevention (CDC), the prevalence of Autism in the USA is 1 in 36 children (as of 2020). The rate of Autism’s prevalence has grown significantly over the years from 1 in 150 in 2000 to 1 in 36 in 2020. Currently, Autism spectrum disorder has no single known cause. Methodology: In this work, an author analyses the causes of Breast Cancer and Autism using a novel method presented in the article “Method of Combining Multiple Researches to Determine Non-Infectious Disease Causes, Analysis of Depression and Celiac Disease Causes”. The method uses a special algorithm based on math which allows to find disease causes for a specific non-infectious disease using the results of multiple research regarding the risk factors of the disease. The use of the method requires finding the number of causes for a specific non-infectious disease using a special formula and data on the incidence rate of disease in a specific population. These disease causes are two or more physiological changes beyond approximately 1-sigma interval which if they co-exist long enough must trigger the non-infectious disease (triggering is not optional). After that, the method requires finding disease causation factors out of multiple risk factors found for a disease. Only some of the risk factors are real disease-causing factors. This is achieved using a set of special disease causation criteria discussed in the article above and provided here for reference as well. The method often enables the identification of a few dozen disease causation factors, and potentially more, for a non-infectious disease based on existing medical research. These disease causation factors all point to the same set of a limited number of physiological parameter changes beyond 1-sigma. The number of these physiological changes usually varies depending on the non-infectious disease from a minimum of 2 to a maximum of 6 or very rare. In this case, the few dozen of disease causation factors make changes (we can say “point”) to the same set of 3 physiological parameters (for example). The method then allows to find these physiological parameter changes (which are the real cause of the non-infectious disease) using a property based on math. After this, the method allows to determine which physiological parameter of this group is impacted by each of the dozens of disease causation factors previously found. The method allows to group of these factors according to the physiological parameters they impact. The disease causation factors taken out of each group of these factors and combined together will cause a change beyond 1-sigma to all required for disease-triggering physiological parameters. These combinations of disease causation factors applied long enough will cause a non-infectious disease. The occurrence of the disease causation factors is random but once they act together the non-infectious disease triggering is a must unless the factors are removed fast enough. The final step of the method is validation of its results using other research or the already discussed disease causation criteria in order to eliminate any human errors in steps of the method that could be potentially made. Once the simultaneously taking place physiological changes causing a non-infectious disease has been found the method allows to build a hypothesis of the disease pathology by using them and “connecting the dots”. The example of this process is shown in the work as well. The hypothesises of Breast Cancer and Autism pathologies are proposed as examples of this. The article introduces the basics of the novel method, provides required formulas for calculations and then moves to a detailed analysis of two non-infectious diseases - Breast Cancer and Autism. As the method is novel the appendix has an analogy to explain the idea of the method at “high level”. The author’s introduction to the method will allow other medical researchers to use their own and existing research to determine the causes of non-infectious diseases as per the presented model, using a simple algorithm. Results: Using this method and applying over 29 existing selected studies at the same time an author analyses Breast Cancer and as a result, the work concludes that the next combination of physiological parameters changes beyond the 1-sigma interval (slightly less, actually) causes Breast Cancer if the changes coexist long enough. This combination for Breast Cancer consists of 4 physiological changes that should coexist at the same time: Increased DNA strand breaks (beyond 1-sigma), TNF-alfa increased (beyond 1-sigma), Leptin level increase(beyond 1-sigma), High estradiol (beyond 1-sigma). This disease-causing combination is the same for all individuals with Breast Cancer but the factors which can cause them can vary significantly and some are determined in this research. Investigating Autism causes by applying over 34 existing selected studies using this novel method the author determines a set of physiological parameter changes beyond the 1-sigma interval (slightly less, actually) which if coexisting long enough are triggering Autism. Using the method an author shows that Autism is caused by 2 physiological parameter changes (beyond 1-sigma) combinations for boys and 3 physiological parameter changes (beyond 1-sigma) combinations for girls at the present time. These combinations of physiological parameter changes consist of: 1) Insulin Resistance increase (beyond 1-sigma), 2) Increase in TNF – Alfa (beyond 1-sigma), 3) low Iron (beyond 1-sigma). Low iron is likely required to trigger Autism only in girls as the 3rd physiological change. This combination of physiological changes that causes Autism can be developed under the action of a variety of different factors some of which are determined in this research. The differences in the number of physiological parameters that are causing Autism in boys and girls (2 for boys vs 3 for girls) explain why girls’ rate of Autism is much less than in boys as more physiological changes are required to trigger the non-infectious disease the less its incidence rate [1]. This research determines that there used to be around 7 physiological changes required to cause Autism in the past, in the 1980s and earlier. However, some external factors have removed multiple defensive mechanisms by moving some physiological parameters beyond 1-sigma to a new “norm” and left us at present time with only 2 physiological defenses for boys and 3 physiological defenses for girls as shown in this work. This removal of physiological defenses has caused a significant rise in Autism in recent years. Conclusion: In this article, it is shown how research that estimates the risk of non-infectious disease can be used to determine disease caused as a set of physiological parameters changes beyond 1-sigma (actually, slightly less than this interval). The process of determining which physiological parameter is impacted by a specific external factor has been shown. It was also shown how certain different combinations of external factors can cause a disease if they act together and how to determine these disease-causing factors combinations. Overall, the method presented here allows to increase the efficiency of existing medical research in finding non-infectious disease causes and also stresses that non-infectious diseases have a multi-factorial cause.