Two coexisting physiological changes which trigger Depression. Discovery with a novel method of finding non-infectious disease causes which combines multiple research

Abstract

Depressive disorder (also known as depression) is a common mental disorder. It involves a depressed mood or loss of pleasure or interest in activities for long periods of time. It causes severe symptoms that affect how a person feels, thinks, and handles daily activities, such as sleeping, eating, or working. Depression is highly prevalent and often a chronic or recurring problem that interferes with work and family. The depression erodes the motivation, energy and enjoyment needed to nurture and sustain social and marital relationships, parenting, etc. Risk factors and causal mechanisms involved in depression have implicated a wide range of genetic, neurological, hormonal, and endocrinological factors that may play a role in underlying vulnerability or in the processes by which stressors trigger depression in some people. A dominant model of the neurobiology of depression that has emerged in recent years emphasizes the underlying dysregulation of the body’s response to stress, involving the neuroendocrine system and brain responses. Depression is most commonly found among those facing chronically stressful conditions, such as social disadvantage and distressed relationships or lack of supportive and intimate relationships. The literature on biological, environmental, and personal risk factors for depression also shows that not all individuals who have been exposed to risk factors for depression develop the disorder. Overall the research suggest that the causes of depression likely include genetic, biological, psychological, and environmental factors but the exact causes of depression are still unknown. In this work an author is introducing a novel method of finding non-infectious diseases and then applies it to analyze the causes of Depression.This method has been already published in other article [2] and also applied to other non- infectious diseases, for example to Breast Cancer disease and has shown results which are matching to existing medical facts. The method is using a special algorithm based in math and allows to find disease causes for a specific non- infectious disease using results of multiple researches regarding risk factors of the disease. The method is based on a model presented in the article “A Connection between Factors Causing Diseases and Diseases Frequencies: Its Application in Finding Disease Causes” (Alan Olan, Journal of Clinical Trials, Vol.13, Issue 4.) which is confirmed by empirical data.This method allows to combine dozens of such researches together with dozens of researches on biochemistry and physiology to extract implicit information on the causes of non-infectious disease out this entire research. The use of method requires to find number of causes for a specific non-infectious disease using a special formula and data on disease frequency (usually the incidence rate) in specific population. These disease causes are two or more physiological changes beyond approximately 1-sigma interval which if they are co-existing long enough must trigger the non-infectious disease (triggering is not optional). After that the method requires to find disease causation factors out of multiple risk factors found for a disease. Only some of risk factors are real disease causing factors. This is achieved using a set of special disease causation criteria discussed in the article by Olan A. [1] above and provided here for a reference as well. The method often allows to find few dozen of disease causation factors and more for a non-infectious disease out of existing medical research. These disease causation factors all point to the same set of limited number of physiological parameters changes beyond 1-sigma. The number of these physiological changes usually vary depending on the non- infectious disease from a minimum of 2 to a maximum of 6 or very rare more. In this case the few dozens 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 properties based in 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. Then method allows to group these factors according to the physiological parameter 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. 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 errors in steps of the method which we could potentially make. 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” using the existing medical research on physiology, immunology, etc. The example of this process shown in the work as well. The hypothesis of Depression pathology is proposed as one example of this. The article introduces to the basics of the method, provides required formulas for calculations and then move to a detailed analysis of Depression disease. 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 presented model, using a simple algorithm. Results: Using this method and applying multiple existing selected studies at the same time an author analyses Depression and as a result the work gives the causes of Depression disease as a set of two physiological parameters changes beyond 1-sigma interval (slightly less, actually) and also as a set of disease causing external factors which combinations in an individual must cause Depression as per presented model. The research has found that a reduction of GABAergic inhibition beyond ~1 sigma interval and lowering BDNF level below ~1 sigma interval will cause the Depression if both conditions coexist long enough. If both of these physiological changes coexist long enough the disease triggering is not optional it is a must. These 2 physiological parameters can be changed by different combinations of factors some of which are found in this research and as the combination of factors can vary greatly this creates an effect of randomness of Depression causes which we usually observe while the underlying physiological reasons for the disease stay the same. Some of the disease causation factors found related to reduction (only under specific conditions) of GABAergic inhibition such as Epilepsy, being a women in late transition to menopause, etc. while factors from the other group reduce (under specific conditions) the BDNF levels such as Insomnia, Social phobias and others. It means that under certain conditions specified in the article the presence of Epilepsy and Insomnia would cause a Depression after some time, but if there is Epilepsy and there is Social phobias (under specific conditions) only the individual will get sick with Depression too as all these combinations affect the real underlying cause - this unique combination of 2 physiological parameters changes which causes the Depression. The facts that triggering Depression requires only a coexistence 2 physiological changes explains the high prevalence of Depression. The model [1] the current research is based on states that the more incidence rate of non-infectious disease the less number of coexisting physiological changes beyond 1-sigma (actually, slightly less) is required to trigger the disease. This fact connects epidemiologic data and physiological underlying causes of non-infectious disease.