Nonclinical Preventive Measures of Parkinson's Disease (PD): Identifying Key Lifestyle, Demographic, and Environmental Factors

Nonclinical Preventive Measures of Parkinson's Disease (PD): Identifying Key Lifestyle, Demographic, and Environmental Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Nonclinical Preventive Measures of Parkinson's Disease (PD): Identifying Key Lifestyle, Demographic, and Environmental Factors Niharika Namburi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5679320/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Parkinson’s disease (PD) is a neurodegenerative disorder with a rising global prevalence. While 15–25% of cases are hereditary, the rest are attributed to exogenous factors, such as environmental exposures and lifestyle choices. This study explores the relationships between various environmental, lifestyle, and health-related factors and PD risk via data from the Fox Insight database and analyzes descriptive statistics, logistic regression, and predictive modeling techniques. Key findings show that older age, male sex, lower BMI, unemployment (including both retired and unemployed individuals), and occupational pesticide exposure increase the risk of PD. Interestingly, higher BMI was associated with a reduced risk of PD, suggesting a potential protective effect, althoughthis may be influenced by reverse causality. Additionally, vigorous physical activity was found to be linked with an increased risk of PD, which could also reflect reverse causality, where individuals diagnosed with PD may increase their activity levels in response to their condition. These results highlight important modifiable factors for PD prevention and suggest areas for further research, particularly in understanding the complex interactions among lifestyle factors, environmental exposures, and disease onset. Biological sciences/Neuroscience Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Health sciences/Health care Parkinson’s Disease Predictive Modeling Lifestyle Factors Environmental Exposures Nonclinical Prevention and Public Health Risk Factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Literature Review Parkinson’s disease (PD) is the second most recurring brain aging disease, with a history of 200 years, after James Parkinson’s disease (Kulcsarova et al., 2024; Deliz et al., 2024). It is the second most prevalent neurodegenerative disease globally, following Alzheimer's disease, and it is estimated to impact approximately 1% of individuals over 60 years of age and may impact approximately 5% of individuals over 85 years of age (Choo et al., 2020; Crooks et al., 2023; Czarnik et al., 2024). However, it is estimated that 5–10% of people with PD are diagnosed before the age of 50. Approximately 15 to 25% of individuals with Parkinson’s disease are estimated to have a family member who also has this condition. (National Institute of Neurological Disorders and Stroke, 2023). The pathological definition is “the result of selective degeneration of dopaminergic neurons in the substantia nigra, which causes a decreased level of dopamine in the striatum and leads to abnormal motor control” (Chia et al, 2020). Other definitions include PD as pathological misfolding of α-synuclein aggregates, also known as Lewy body deposits, that impact the central, peripheral, and enteric nervous systems (Klann et al., 2022). This enteric nervous system underlies the “brain‒gut” axis, a bidirectional pathway that emphasizes the role of inflammation and the microbiome in PD neurodegeneration (Klann et al., 2022). Clinically, Parkinson's disease is a neurodegenerative disorder that primarily affects the motor system, causing symptoms such as tremors, rigidity, bradykinesia, and postural instability. In addition to the well-known motor symptoms of Parkinson's disease, several nonmotor symptoms can significantly impact the health-related quality of life (HRQoL) of individuals (Bloem et al., 2021; Kulcsarova et al., 2024; Deliz et al., 2024). These nonmotor symptoms can include depression, anxiety, cognitive changes, and sleep disturbances (Bougea, 2024). The World Health Organization (WHO) defines HRQoL as “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns. HRQoL assessment includes motor and physical skills, mental health, somatic perception, and socioeconomic conditions” (Crispino et al, 2021). Despite many studies on the etiology of PD, identifying the cause in most patients is difficult; however, various genetic etiologies have been identified. The etiology of PD is multifactorial, with variations in geography, age, sex, genetics, and environmental factors (Deliz et al, 2024, Berg et al., 2022). In the last decade, significant advancements in genetics coupled with extensive epidemiological studies have led to comprehensive knowledge of the genetic, behavioral, and environmental influences involved in the development and progression of Parkinson’s disease. Bolem and Kelin (2021) mentioned that treatment goals vary for each person, emphasizing the need for personalized management. At present, no therapy can slow or arrest the progression of Parkinson's disease. Owing to scientific and technological advancements in the medical field, the average life span of humans has increased globally. Simultaneously, there has been an increase in chronic diseases in the aging population. PD is a rising trend highly associated with the aging population. Ou et al. (2021) conducted a global study related to PD in 204 countries from 1990--2019. This study highlighted occupational factors, unhealthy lifestyles, and environmental pollution, indicating that more effective strategies are needed to address this global health challenge. While the exact cause of Parkinson's disease is not fully understood, researchers have identified a variety of risk factors and potential preventive strategies that may help reduce the risk of developing this debilitating condition (Müller-Nedebock et al., 2023). This paper aims to focus on preventive strategies and how they can be advocated to patients and their families so that they become familiar with major diseases such as diabetes, breast cancer, and HIV/AIDS. Crooks et al. (2023) conducted a scooping review on public perceptions and awareness of PD. They reported that there was a significant lack of understanding of the disease among the public and that few educational resources were available. The study concluded that public perceptions and awareness are crucial for early diagnosis, effective management, and improving overall quality of life. This can influence public support for research, funding, and legislative/policy decisions. Parkinson's Disease Preventive Measures In the current technology-driven world, we believe that spreading information is relatively easy. However, that is not the case in educating the public regarding Parkinson’s disease. A considerable amount of noise is needed in public awareness of PD, such as in the case of breast cancer. Charlotte Haley, a frustrated 68-year-old woman, led the pink ribbon movement to bring awareness among the public to attract the attention of legislators (Dorsey et al., 2020; De Miranda et al., 2022). Similar movement is necessary in the case of PD. Scholars have estimated that the number of people affected by PD has grown exponentially since 1990. It ranged from 2.5 million in 1990 to 6.2 million in 2015 and is expected to double to 12.9 million by 2040 (De Miranda et al., 2022). Males are affected by Parkinson's disease almost 1.6 times more than females are affected, with rates of 61.21 per 100,000 for males and 37.55 per 100,000 for females (Boina, 2022). Many public and private funders have poured their resources into finding a cure for PD, especially with respect to genetic factors (both inherited and idiopathic PD). However, microscopic studies on nongenetic factors and preventive measures have been conducted (De Miranda et al., 2022). De Miranda et al. (2022) studied mainly environmental contaminants, such as pesticides, metals, and industrial chemicals. They opined that such contamination increases the risk of developing PD. According to their study, approximately 27% of PD cases are heritable, and exogenous factors largely influence the remaining percentage. They developed a “PD prevention agenda” to highlight primary prevention along the lines of preclinical/basic research and clinical/translational research. Rajan and Kaas (2022) reported that physical activity, an early active lifestyle, high serum uric acid, caffeine consumption, tobacco exposure, the use of nonsteroidal anti-inflammatory drugs, and the use of calcium channel blockers along with a Mediterranean diet reduce the risk of PD. These are considered protective factors for PD. Additionally, evidence has shown that caffeine intake combined with physical activity can act as primary prevention and disease-modifying strategies in PD patients (Belvisi, et al., 2020). In contrast, the combination of hereditary factors such as pesticide exposure, farming, high dairy consumption, and head trauma injuries are known to increase the risk of PD (Belvisi, et al., 2020; Rajan & Kaas, 2022). Patients with Parkinson's disease who arrive at the emergency department due to injuries tend to have generally poor health and a high number of underlying conditions. This allows the identification of comorbidities along with demographic and socioeconomic factors as potential risk factors for PD (Al-Hakeem et al., 2024). Individualized care could help identify risk factors and prevent them from improving quality of life. Chen et al. (2024) prospectively investigated the associations of PD with physical activity, sleep patterns, and the combination of these two risk factors. Both high physical activity and good sleep are associated with a lower risk of developing PD. Additionally, high physical activity and poor sleep are associated with lower risk. These results were consistent among both sexes and across different age groups. Interventions involving the combination of physical activity and ideal sleep patterns would be promising prevention strategies. Czarnik et al. (2024) studied the effects of the brain‒gut axis and the special role of diet in the occurrence of PD. The underlying emphasis is reducing inflammation by maintaining a healthy diet. In particular, the Mediterranean diet consists of plant products, olive oil, fish, and seafood, with less red meat. This study demonstrated that modifications to gut microbes can have a positive impact on neuropsychiatric health. Poulia (2024) conducted a prospective study among 126283 participants from the UK Biobank cohort to explore the role of plant-based diets in the prevention of PD. The results revealed that greater consumption of vegetables, nuts, and tea was linked to a reduced risk of PD by 28%, 31%, and 25%, respectively. Klann et al. (2022) reviewed the literature to shed light on the relationship between the gut microbiota and PD. This study revealed that providing pre- and probiotic supplementation during adolescence improved resilience toward neurodegenerative disorders by reducing inflammation and promoting neurogenesis. Klann et al. (2022) proposed that evaluating the composition of the gut microbiota could help in detecting the early onset of PD. Balakrishnan et al. (2021) reported that oxidative stress and neuroinflammation are significant factors responsible for PD progression. This study revealed that naturally derived phytochemicals and their derivatives are protective factors with no adverse effects when consumed. For example, phytochemicals such as chrysin are found in honey, vanillin is found in vanilla beans, and caffeic acid is found in coffee, spinach, tomatoes, and berries. Using natural phytochemicals as a part of the diet, supplements or novel therapeutic interventions would help prevent PD or slow its progression. Clinical Preventive Measures At present, there is no known cure for PD (National Institute on Aging, 2022; National Health Services, 2022; Scorza et al., 2021). However, treating disease symptoms predominantly focuses on the dopaminergic pathway via levodopa drugs, deep brain stimulation (DBS) procedures, and stem cell transplants (Scorza et al., 2021; Bjørklund et al., 2020; Crooks, et al., 2023). Some novel drug targets, including lipid peroxidation, protein oxidation, DNA damage, and mitochondria, are still in progress (Bjørklund et al., 2020). The clinical aspects are beyond the scope of this study. Nonclinical Preventive Measures “Prevention is better than cure” (The Lancet Psychiatry, 2022). The modern version of the Hippocratic Oath, which is the most popular among medical graduates, says, “I will prevent disease whenever I can, for prevention is preferable to cure” (Newhouse, 2021). In health care, prevention is categorized into three main categories: primary, secondary, and tertiary (AbdulRaheem, 2023). Primary prevention focuses on preventing diseases or injuries from occurring first. This paper focuses on the primary prevention component of PD. For example, individuals should be educated regarding healthy eating and safe habits. Secondary prevention focuses on addressing the slow progression of the disease or reducing the impact of disease or injury that has already occurred. For example, modified occupational conditions where individuals can return safely to their jobs. Finally, tertiary prevention focuses on reducing the long-term impact of an illness or injury that is already present. This includes programs that involve support groups to share strategies for living with chronic illnesses (AbdulRaheem, 2023). At the community and population levels, telehealth and genetic sequencing could help marginalized groups attend preventive programs and ultimately aim for the early identification of modifiable risk factors and timely implementation of effective interventions (Lau, 2023). Vellata et al. (2021) systematically reviewed the effectiveness of telemedicine and telerehabilitation with PD patients. This study concluded that telerehabilitation and telemedicine are feasible for PD patients who have positive changes in their preassessment perceptions and high satisfaction levels in managing their nonmotor and motor-related symptoms and overall quality of life. These options help minimize barriers such as distance, time, and cost. Kwok et al. (2023) conducted a random control trial of mindfulness meditation exercises in PD patients, and the results showed that mindfulness meditation proved to be a promising strategy for managing depressive symptoms and improving cognitive performance among mild to moderate PD patients. Modifiable risk factors may impact cognitive outcomes in PD patients, and evidence has shown that aerobic exercise improves cognition in PD patients (Carlisle et al., 2023). Mobile health (mHelaht) technology can collect both clinical and nonclinical information and exchange that information with existing health informatics systems such as electronic health records (Bouça-Machado et al., 2021). One of these advantages is continuous monitoring, which can also help empower patients to self-manage PD or practice healthy lifestyles such as walking, sleep patterns, and self-report questionnaires to track progress and provide an interactive platform for both healthcare professionals and patients and caregivers to deliver personalized care (Bouça-Machado, et al., 2021). Choo et al. (2020) conducted a survey using the Knowledge and Perception of Parkinson’s Disease Questionnaire (KPPDQ) at a university hospital neurology clinic and reported that there are knowledge gaps, misperceptions and perspectives on PD. Additionally, research on PD-related stigma remains scarce, and further research is recommended to understand the magnitude of knowledge gaps and perspectives in individual and community areas (Choo et al., 2020). Factors affecting the study (independent variables) Age, sex, race, BMI, geographical location, socioeconomic, diet, lifestyle, physical activity, social engagement, mental stimulation, sleep patterns, daytime sleepiness, fatigue, anxiety, depression, comorbidities (such as vascular risk, hypertension, diabetes, coronary heart disease, hypercholesterolemia), medications, technology acceptance (wearables, sensors, smartphone applications), occupation, urban or rural living, pesticide exposure (such as amphetamine or methamphetamine, paraquat, and chlorpyrifos), industrial solvents (such as trichloroethylene), drinking water contamination, living near industrial areas, air pollution, head injuries/traumatic brain injuries, lack of awareness, education, and stigma around neurodegenerative disorders are some of the significant factors mentioned in various studies. Most of the studies used demographic, socioeconomic, and lifestyle variables (such as age, sex, ethnicity, comorbidities, education, healthy lifestyle, and BMI) to determine the associations with the development of PD in the future or to identify PD-risk individuals. Chen et al. (2024) reported that participants who developed PD during the follow-up period were typically older; male; nonsmokers; and users of hypertension medication, as well as those with diabetes. Compared with those who exercised less, those who exercised more were often men and White, had a lower BMI, did not smoke, spent less time sitting, ate healthier, and had fewer chronic illnesses such as high blood pressure and diabetes. A similar trend followed in terms of sleep patterns and daytime sleepiness. Individuals with healthy sleep characteristics, such as larks (morning chronotype), 7–8 hr/day sleep, no insomnia, no snoring, and no frequent daytime sleepiness, had a low risk of PD development. Participants with high total physical activity (including work, transportation, chores, gardening, and leisure) had a 27% lower risk of developing Parkinson's disease than did those with low physical activity, even after adjusting for various factors (Chen, et al., 2024). Boina (2022) reported that functional and aerobic activities can strongly slow the progression of Parkinson’s disease. Choo et al. (2020) reported that most caregivers and PD patients are unable to recognize nonmotor symptoms such as pain, a reduced sense of smell, urinary problems, and visual hallucinations in the early stages. This study highlighted the major misconception that PD has curative treatment due to stem cell procedure advertisements. Crooks et al. (2023), in their scoping review, identified a lack of awareness among the public, scarce education resource availability, and stigma around neurodegenerative disorders, specifically PD, and found associations between PD and depression, isolation, and loss of independence. Chevinsky et al. (2024) reported that diet has a significant effect on reducing the risk of PD. Various factors, such as age, toxic substances, oxidative stress, alcohol, physical activity, medications, or drugs, disrupt the gut microbiome, and an unhealthy lifestyle is associated with increased inflammation, which in turn increases the risk of PD. De Miranda et al. (2022) reported that sex/gender, as a biological variable in toxicant exposure, and PD are more prevalent in men than in women because men are more exposed to risk factors such as pesticide applicators and factory workers. However, one study from Japan showed that PD is more prevalent in women, possibly because Japan has more female farmers (De Miranda et al., 2022). This evidence shows that geographical and cultural factors may play a role in PD incidence. This study emphasized that combined exposure to solvents, pesticides, metals, and other industrial byproducts must be considered risk factors for PD, as the additive or synergistic effects of these compounds influence their toxicokinetics and ultimately their combined neurotoxicity. The Cures Act in U.S. law emphasizes that the use of mobile devices, wearable technology, and biosensors offers the potential to more effectively involve individuals in managing their healthcare (De Miranda et al., 2022; Bouça-Machado et al., 2021). Gaps in the Literature De Miranda et al. (2022) failed to clarify exogenous factors (nongenetic) such as pathogenetic infection, head trauma, diet, pharmaceutical, supplement, drug use, and other physiological stressors interact with each other or how individuals become more at risk of developing PD if more than one factor is present. There are few gaps in assessments of the chronic consumption of low-level pesticide-laden foods and other commonly used industrial chemicals, such as dry cleaners, mechanics, computer chip manufacturers, etc. More interdisciplinary research involving the combination of multiple factors and effective preventive strategies to reduce exposure to environmental toxins is needed. Chen et al. (2024) focused on physical activity and sleep pattern combinations; however, other lifestyle factors and their interactions have not been discussed thoroughly. More research is needed to establish dietary interventions and specific mechanisms, as the relationship between diet and PD is complex and varies from person to person (Poulia, 2024). Additional research on human subjects is needed to yield consistent results and significant findings for a better understanding of the role of the microbiome‒gut‒brain axis in PD. Further studies are needed to perform a comprehensive evaluation of the role of natural phytochemicals (such as chrysin and caffeic acid) in neuroprotective or therapeutic activities in PD (Balakrishnan et al., 2021). The Kwok et al. (2023) study control group did not receive any intervention, which limits the ability of mindfulness, and the lack of long-term follow-up fails to capture the lasting and consistent effect of mindfulness meditation in PD patients. Additionally, the sample size is relatively small, limiting the generalizability of the results. User experience (of patients and caregivers) with mobile-based monitoring systems has not been explored in depth, and there is a need for larger, diverse research studies to confirm the effectiveness of mobile-based monitoring on quality of life and patient outcomes. We need population-based prospective studies to understand disease incidence trends over time and to compare these trends with environmental factors. This includes assessing the air, water, and food that people consume and making environmental testing records freely available to researchers (De Miranda et al., 2022). The literature on public awareness is very limited, especially since public perceptions of PD vary across different cultures and regions. Limited research has been conducted on PD awareness campaigns, and further research incorporating patient perspectives and caregivers in public awareness campaigns is needed. Review of Existing Methodologies The literature review encompasses a wide range of research methodologies related to PD, such as systematic and critical reviews, cross-sectional studies, retrospective analyses, cohort studies, quantitative studies, randomized controlled trials, and specific topic reviews. De Miranda et al. (2022) performed a systematic and critical review to identify the environmental factors contributing to Parkinson’s disease (PD). They analyzed global epidemiological data on contaminant emissions to estimate PD incidence. Preventive strategies such as mindfulness meditation and physical exercise can help individuals manage PD symptoms by reducing stress, improving mental health, and improving overall well-being. Similarly, Ahern et al. (2024), Belvisi et al. (2020), and Bjorklund et al. (2020) conducted systematic and critical reviews to evaluate behavioral change interventions, modifiable risk factors, and preventive strategies/treatments. Cohort studies such as Chen et al. (2024) explored correlations between physical activity, sleep patterns, and disease incidence. Qualitative studies, such as Chen et al. (2023), identify barriers to and facilitators of palliative care delivery. Randomized clinical trials, such as that of Kwok et al. (2023), compare mindfulness meditation and exercise interventions. Cross-sectional studies, such as that of Lee et al. (2024), have examined the relationships between caregiver involvement and self-care in patients. McDonald et al. (2024) and Al-Hakeem et al. (2024) conducted retrospective analyses to investigate the impacts of primary care continuity and comorbid conditions on health outcomes and emergency visits, respectively. Furthermore, reviews on topics such as the gut‒brain axis (Klann et al., 2022) and public perceptions of Parkinson's disease (Crooks et al., 2023) provide comprehensive insights into various factors influencing disease management and prevention. Gaps in Methodologies De Miranda et al. (2022) could have used more comprehensive data from diverse populations; this study focused on only sex in their PD prevention agenda. The incorporation of longitudinal studies could reveal causality between environmental exposure and PD. Using geospatial data would help to track and improve the accuracy of identifying specific environmental risks (De Miranda et al., 2022). Chen et al. (2024) conducted a prospective cohort study using the UK Biobank and focused mainly on the risk factors for physical activity and sleep patterns. It is geographically constrained, and the results are not generalizable. As these study data are self-reported, there might be issues such as recall bias and inaccuracies. Studies that focus on diet components are mostly qualitative and need to be conducted thoroughly to determine the effects of interventions. In regard to dietary interventions, even though some prospective studies have been conducted, they are mostly confined to one geographic location, which makes it difficult to generalize the results. Age, sex, race, food habits, allergies, different countries' diets, geographical locations, etc., make implementing specific mechanisms somewhat difficult (Chen et al., 2024; De Miranda et al., 2022; Bhidayasiri 2024). Review of Existing Theories Most of the articles in the literature review presented empirical findings, reviewed literature, or discussed practical applications without explicitly proposing a new theory. The following are some of the highlights from those articles that underscore the factors affecting this study: Chen et al. (2024) suggested that existing theories related to the neuroprotective effects of physical activity and the role of sleep in neurodegeneration provide empirical support for both. This highlights the combined benefit of these lifestyle factors in reducing PD risk, suggesting that interventions targeting physical activity and sleep quality could be promising strategies for PD prevention. Further research is needed to explore the underlying mechanisms, long-term impacts, and effectiveness of such interventions across diverse populations. Prevention strategies involving a diet with anti-inflammatory and neurotransmitter components could reduce the risk of PD. A Mediterranean diet, such as simple fiber consumption, impacts PD progression (Czarnik, et al., 2024). Cognitive decline in PD patients could be influenced by modifiable risk factors such as physical activity, diet, mental stimulation, and social engagement (Carlisle, Medina, & Holden, 2023). Bouça-Machado et al. (2021) suggested that mobile-based systems can effectively monitor Parkinson's disease (PD) symptoms remotely, providing continuous and real-time data that can enhance disease management and patient care. Some general existing theories about Parkinson’s disease include behavioral interventions, ethical constructs, cognitive ability, prevention, and management of falls/fear of falling in PD patients, etiology, and overall ways to improve quality of life. Ahern et al.’s (2024) systemic review is based on the theoretical domain framework (TDF) to understand and implement behavior change interventions in Parkinson’s disease patients. This framework consists of 5 key domains, namely, “behavioral regulation,” “beliefs about capabilities,” “social influences,” “reinforcement,” and “goals,” to help with disease management. The authors emphasized that most interventions were multicomponent and involved education, behavioral techniques, and support groups. The authors use physical activity and exercise variables to explain the framework and how it improves health in PD patients. The PD patient group tends to be 30% less active than the control group, i.e., health age-matched (Ahern et al., 2024). This review also highlighted that depression, apathy, and similar nonmotor features are barriers to physical activity, such as exercise, in PD patients. To motivate and improve overall health in PD patients, especially exercise self-efficacy and adherence, the authors underscore the identification of behavioral change interventions that, in turn, lead to improved strength, fitness, and quality of life (QoL) and how these interventions map to TDFs. It includes education programs such as brochures, weekly lectures on suitable exercises, and overcoming barriers; behavioral techniques such as decision-making, problem-solving, and identifying potential barriers; and technology usage involving activity trackers, virtual coaches, tailored content to a person’s needs, and motivation. Support groups through peer-group online sessions and resource availability in the environmental context help in action planning and social influences. Maggi et al. (2024) focus on the concept of the “Theory of Mind” (ToM), which refers to the ability to understand and attribute mental states such as beliefs, intentions, desires, and emotions to oneself and others. This article explores how cognitive impairments, especially memory problems, affect this ability in individuals with mild cognitive impairment (MCI) and Parkinson's disease (PD). This study highlights how memory impairment impacts ToM in these populations, suggesting that memory difficulties can significantly influence social cognition and interpersonal interactions in people with MCI and PD. Basas and Gozum (2023) proposed a culture of encounters (CoE). This theory emphasizes the importance of compassionate care in healthcare, particularly for individuals with Parkinson's disease. It highlights the ethical aspect of healthcare by creating a culture of encounters between patients and providers characterized by empathy, understanding, and mutual respect. The theory aims to improve the overall care experience and support the emotional and psychological well-being of patients with Parkinson's disease. It primarily advocated for a holistic, patient-centric approach to care. Lee et al. (2024) used Riegel's theory of self-care for chronic illness to highlight the complex and often contradictory interactions between different developmental factors. This study underscores the role of individuals in actively managing their PD through self-care practices. This study examines how the relationship between PD patients and their caregivers influences patients’ ability to engage in self-care. These findings underscore that supportive and positive caregiver relationships significantly improve patients' self-care behaviors, leading to better disease management and overall well-being (Lee et al., 2024). Chen et al. (2023) conducted semistructured interviews via the social ecological model (SEM) framework to identify the barriers and facilitators influencing palliative care for PD patients and their caregivers living in China. The World Health Organization (WHO) defines palliative care (PC) as “an approach (through the prevention and relief of suffering, early identification, impeccable assessment, and treatment) to improve the quality of life of patients and their families, those who are facing the pain and other physical, psychological and spiritual problems associated with a life-threatening illness” (Chen et al., 2023). Palliative care facilitators are identified at the individual (PD patients and caregivers' needs; providers’ PC knowledge), interpersonal (social support), organizational (encouraging connectors such as nurses), community, cultural, and policy levels. Barriers include PC misconceptions economically at the individual level; miscommunication at the interpersonal level; a lack of access to PC resources; death; and ethical dilemmas surrounding PD at the community, culture, and policy levels. Liu et al. (2022) conducted a systemic review on the prevention and management of falls and fear of falling (FoF) in PD patients and proposed a novel fall prevention theory. According to their model, physical exercise is considered one of the strong factors for improving patients’ fitness, reducing falls, and reducing their fear of falling, as recommended for primary prevention. In the case of mild PD symptoms, the combination of medication and low-intensity exercise, such as tai chi or walking, is recommended. This theory also emphasizes the active involvement of the entire community. Chen et al. (2022) discussed the Braak and dual-hit theories to provide theoretical principles to better understand the environmental triggers of PD. This theory emphasizes that olfactory structures and the gut are mucosal interfaces of humans exposed to environmental exposures, e.g., heavy metals such as manganese, and organic solvents such as trichloroethylene in the PD context. These environmental risk factors damage dopamine-containing neurons in the brain, causing neurodegeneration in PD-risk individuals (Chen et al., 2022). Munoz-Pinto et al. (2021) proposed a unifying theory that connects various aspects of neuromicrobiology to explain PD etiology. The authors emphasized that microbial infections and imbalances in the gut microbiome are potential factors in PD onset and progression, thus targeting the gut‒brain axis. This theory explains that microbial factors cause neuroinflammation and neurodegeneration and, in turn, lead to PD symptoms. Ying and Vasanthi (2022) conducted a cross-sectional survey in the Malaysian context by using a self-administered and validated questionnaire to investigate exercise beliefs among PD patients during disease progression. This study used the health belief model (HBM) to evaluate the relationship between beliefs and preventive health behavior practices such as exercise. This study emphasized that despite strong supporting evidence of exercise benefits for PD patients, the National Parkinson Foundation reported that more than half of people (53%) with Parkinson’s disease do not exercise regularly. This lack of exercise seems to worsen their quality of life, making them physically weaker, leading to disease progression, and placing a burden on their caregivers. Gaps in Theories Many theories have been proposed or used to understand the biological and neurological aspects of Parkinson’s disease (PD). Ahern et al. (2024), Liu et al. (2022), and Ying and Vasanthi (2022) emphasized the role of physical activity, such as exercise, in reducing PD progression. The theories of Chen et al. (2022) and Munoz-Pinto et al. (2021) highlight the effects of environmental triggers and microbial infections on the gut microbiome, respectively, to evaluate the risk of potential factors associated with PD onset and progression. Maggi et al. (2024) focused on affected PD patients with mild cognitive symptoms. Basas and Gozum (2023), Chen et al. (2023), and Lee et al. (2024), in their respective theoretical platforms, discuss more behavioral interventions, self-care, and management, patient families, and caregiver burdens related to PD patients. Despite these significant theories and findings, gaps related to the combination of risk factors, the factors that are protective and modifiable, the identification of at-risk PD patients, and preventive measures/interventions involving nonclinical components still exist. Most of the existing theories discuss the role of exercise; however, other factors, such as diet, geographical location, health care provider education, and caregiver education, are missing from a single model. Methodology Introduction The prevalence of Parkinson’s disease (PD) has increased worldwide and is projected to reach approximately 13 million by 2040 (De Miranda et al., 2022). While the exact cause of PD is unknown, only 15–25% of cases are hereditary (NINDS, 2023), and the remaining 75–85% of cases are thought to be linked to exogenous factors such as environmental toxins, head trauma, and lifestyle factors (De Miranda et al., 2022). Hence, there is a critical need to explore and implement nonclinical preventive measures to reduce the risk of Parkinson's disease. By addressing lifestyle factors and environmental exposures and promoting early intervention, we can significantly mitigate the burden of PD on individuals and global healthcare systems. To the best of the literature review and knowledge of the author, no academic studies have attempted to include most of the exogenous factors in one model involving both PD patients and non-PD patients (healthy volunteers). Chen et al. (2023) used SEM theory to identify the barriers and facilitators influencing palliative care for PD patients and their caregivers living in China. However, this study was conducted in a terminal care environment. Few studies have focused on a combination of physical activity and sleep among PD patients. This research attempts to identify the key lifestyle, demographic, and environmental factors and their combinations with the greatest effects on the risk of developing Parkinson’s disease. This insight will allow one to identify high-risk populations and focus on developing public health strategies to mitigate the risk of developing Parkinson’s disease. To explore the exogenous factors, My Research question focuses on the “Do environmental risk factors differ significantly between PD and non-PD individuals?” which would help me to structure hypotheses about whether the combination of key environmental factors impacts the risk of developing PD. These hypotheses were analyzed as a part of the final model, and they were accepted/rejected on the basis of their p value. H1: Demographic and socioeconomic factors differ significantly between PD patients and non-PD patients H2: Lifestyle and behavioral factors differ significantly between PD patients and non-PD patients. H3: Health-related and medical history factors significantly differ between PD patients and non-PD patients H4: Environmental and occupational exposure factors differ significantly between PD and non-PD individuals . To investigate the differences between Parkinson's disease (PD) and non-PD individuals, we hypothesize that significant variations exist in various factors, including demographics, socioeconomic status, lifestyle behaviors, health history, and environmental exposures. In our analysis, we categorized all the predictors into four key groups for clarity and ease of interpretation: The demographic and socioeconomic factors included age, sex, race and ethnicity, education level, employment status, and income level. Lifestyle and behavioral factors, such as alcohol use, smoking status, caffeine consumption, physical activity (both vigorous and moderate), and sleep duration, are considered here. The health -related and medical history factor categories focus on head injury history, anti-inflammatory medication use, and calcium channel blocker (CCB) history. Environmental and occupational exposure factors, including the residential environment (urban or rural), proximity to farms, access to private well water, occupation (e.g., military), toxicant exposure (such as glue and adhesives), and pesticide exposure (from jobs or sprays), are examined. By grouping the predictors in this way, we aim to provide a structured approach to understanding how these factors may differ between PD and non-PD individuals. Methods This analysis utilized Fox Insight, an online clinical study building a large, diverse cohort of people with Parkinson’s disease and age-matched control volunteers who share information about their lived experience, genetics, and variability in Parkinson’s disease via a one-time environmental exposure questionnaire that was deployed between October 2017 and March 2019 (Gottesman et al, 2024). The participants were all 18 years and older and provided informed consent via the Fox Insight website. The study protocol was approved by the New England IRB (IRB#: 120160179, Legacy IRB#: 14–236, Sponsor Protocol Number: 1, Study Title: Fox Insight). (Smolensky et al., 2020 ). All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki and applicable regulatory requirements. The analysis exclusively utilized de-identified data from the Fox Insight database, ensuring adherence to ethical standards for secondary data analysis. The dataset will be analyzed via descriptive statistics, i.e., frequencies, means, and standard deviations for demographic and awareness variables. This was followed by inferential statistical analyses via the chi-square test to explore relationships/associations between environmental factors, lifestyle behaviors, and demographic factors. Finally, multivariate analysis via logistic regression was used to assess the combined effects of multiple risk factors on the likelihood of developing PD. This study conducts an analysis via the statistical software Python for accuracy and efficiency. All the statistical test results were considered significant if the p value was less than 0.05. Data The main dataset named “About You” consists of demographic information of all U.S.-based participants. This dataset contains all 55,000 participants with their basic information (e.g., age, sex, disease status). The one-time Environmental Exposure Questionnaire has 12 datasets (Alcohol, Caffeine, Smoking and Tobacco, Head Injury and Concussion, Pesticides at Work, Pesticides in Nonwork Settings, Residential History, Physical Activity and Sleep, Calcium Channel Blocker Medication History, Anti-Inflammatory Medication History, Occupation, and Toxicant) that were answered voluntarily by some participants from 55,000. All 12 datasets were merged into the main dataset by using a unique participant ID, “fox_insight_id”. Predictor variables A total of 24 predictor variables were selected to analyze risk factors between PD and non-PD patients, after which a prediction model was built. Outcome/target variable : The outcome variable of this analysis was a predictive model that accurately predicted true PD cases. This was measured by splitting the data at a 70:30 ratio, with 70% of the data training a model on PD vs non-PD and testing it on 30% of the data in predicting the true PD cases. Detailed descriptions of the predictor and outcome variables are provided in Appendix A , as are the variable values and descriptions. Data Analytic Plan The initial step of this research consisted of downloading data files from the Fox Insight database. The main dataset “About You” is the base dataset for my analysis, and 11 datasets include the one-time environmental exposure questionnaire. All 11 datasets were merged into the base dataset via the unique identifier “fox_insight_id”. After carefully removing duplicates, preferring not to say, unknown, and missing, the total number of cases in the dataset reached approximately 1198. Exploratory analysis was then performed on this final PD dataset to create a pie chart showing PD cases and non-PD cases. This is performed to observe the differences in the outcome variable. Numerical variables such as age and BMI are shown in pair plots to determine the distribution to check the assumptions (normality, linearity, homogeneity, and homoscedasticity). Categorical variables such as sex, race, and education level were plotted as bar charts to visualize the distributions of various subgroups. A few categorical variables were recoded to make the data more consistent by transforming it into a more meaningful and manageable format. For instance, the race variable is recoded as White vs Nonwhite, and details of all other recoded variables are shown in Appendix A . Several variables in the dataset, including employment and education, were recoded to simplify analysis and improve interpretability. This process involved reducing the number of categories in variables with multiple levels, such as education, to facilitate meaningful statistical analysis. Categories were grouped to address issues with small sample sizes or uneven distributions, as seen with the race variable, where certain groups were underrepresented. Additionally, recoding was carried out to enhance clarity and relevance by aligning the variables with widely used categorizations in the literature and addressing the research questions effectively. All the recoded variable descriptions are shown in Appendix A . Several models were created and analyzed before the logistic regression model was selected as the best model to predict whether a participant is at risk of developing Parkinson’s disease (PD), which is influenced by the combination of predictor variables. Additionally, backward stepwise pseudo-R 2 was performed to filter down the 24 variables to include only selected variables in the model. Owing to an imbalance of the dataset between PD and non-PD cases, SMOTE analysis was performed to resample the minority group (non-PD), and a random forest classifier was used to train and test the balanced dataset to address the bias. Results Statistical assumption testing was performed for continuous predictor variables (age and BMI) to check for normality, linearity, homogeneity, and homoscedasticity, and the variables were evaluated as part of hypothesis testing via nonparametric tests such as the Mann‒Whitney U test for medians/IQRs. Age and BMI were skewed (Fig. 1), which was noted in the development of the logistic regression model. The remaining predictor variables are categorical, and cross-tabulations are performed via chi-square tests and variance inflation factors (VIFs) to detect multicollinearity. Boxplots are used to check assumptions of homoscedasticity or normality for regression. Most of these variables were skewed, which was noted in the development of the logistic regression model. Key Tables Tables 1 and 2 provide an overview of the hypotheses that were supported and not supported in this research on the basis of the significance of p values above and below 0.05. Table 1 Demographic and socioeconomic factor information for PD vs non-PD participants Characteristic, n (%) Non-PD (n = 177) PD (n = 1021) Total (N = 1198) p value Age (Median/IQR) 59.4 (17.2) 65.8 (11.9) 65 (12.7) < .001 Gender < .001 Male 48 (27.12) 584 (57.20) 632 (52.75) Female 129 (72.88) 437 (42.80) 566 (47.25) BMI (Median/IQR) 27.44 (8.46) 25.66 (5.82) 25.82 (6.4) < .001 Race 1.00 White 172 (97.18) 992 (97.16) 1164 (97.16) Non-White 5 (2.82) 29 (2.84) 34 (2.84) Ethnicity 0.78 Hispanic 4 (2.26) 21 (2.06) 25 (2.09) Not Hispanic 173 (97.74) 1000 (97.94) 1173 (97.91) Education 0.31 High Education 129 (72.88) 696 (68.17) 825 (68.86) Moderate Education 41 (23.16) 259 (25.37) 300 (25.04) Low Education 7 (3.95) 66 (6.46) 73 (6.09) Employment < .001 Employed 98 (55.37) 326 (31.93) 424 (35.39) Not Employed 79 (44.63) 695 (68.07) 774 (64.61) Income 0.78 High Income 100 (56.50) 596 (58.37) 696 (58.10) Middle Income 55 (31.07) 291 (28.50) 346 (28.88) Low Income 22 (12.43) 134 (13.12) 156 (13.02) Table 2 Environmental exposure factors (lifestyle and behavioral factors; environment and occupational exposure factors; health-related and medical history) Characteristic, n (%) Non-PD (n = 177) PD (n = 177) Total (n = 354) p value Alcohol 0.43 Yes 151 (85.31) 843 (82.57) 994 (82.97) No 26 (14.69) 178 (17.43) 204 (17.03) Smoking 0.57 Yes 54 (30.51) 337 (33.01) 391 (32.64) No 123 (69.49) 684 (66.99) 807 (67.36) Caffeine 0.99 Yes 118 (66.67) 677 (66.31) 795 (66.36) No 59 (33.33) 344 (33.69) 403 (33.64) Head Injury 0.74 Yes 66 (37.29) 374 (36.63) 440 (36.73) Possibly 21 (11.86) 143 (14.01) 164 (13.69) No 90 (50.85) 504 (49.36) 594 (49.58) Occupation(military) 0.01 Yes 13 (7.34) 158 (15.48) 171 (14.27) No 164 (92.66) 863 (84.52) 1027 (85.73) Pesticide Exposure (JOB) < .001 Yes 13 (7.34) 175 (17.14) 188 (15.69) No 164 (92.66) 846 (82.86) 1010 (84.31) Toxicants (glues/adhesives) 0.46 Yes 41 (23.16) 208 (20.37) 249 (20.78) No 136 (76.84) 813 (79.63) 949 (79.22) Vigorous Physical Activity (hr/wk) 0.03 10 hours 10 (5.65) 69 (6.76) 79 (6.59) Moderate Physical Activity (hr/wk) 0.80 10 hours 17 (9.60) 115 (11.26) 132 (11.02) Sleep Duration 0.69 8 hours 7 (3.95) 47 (4.60) 54 (4.51) Residential 0.01 Urban 170 (96.05) 915 (89.62) 1085 (90.57) Rural 7 (3.95) 106 (10.38) 113 (9.43) Private Well Water* 0.52 Yes 21 (11.86) 143 (14.01) 164 (13.69) No 156 (88.14) 878 (85.99) 1034 (86.31) Pesticide Spray* 0.03 Yes 59 (33.33) 433 (42.41) 492 (41.07) No 118 (66.67) 588 (57.59) 706 (58.93) Located near Farm* 0.45 Yes 30 (16.95) 201(19.69) 231 (19.28) No 147 (83.05) 820 (80.31) 967 (80.72) Anti-inflammatory medication Hx 0.15 Yes 66 (37.29) 321 (31.44) 387 (32.3) No 111 (62.71) 700 (68.56) 811 (67.7) CCB Hx 0.34 Yes 94 (53.11) 499 (48.87) 593 (49.5) No 83 (46.89) 522 (51.13) 605 (50.5) Note. Hx = history, CCB = calcium channel blockers * indicates whether the participants' residential environmental conditions. Univariate and bivariate analyses revealed notable demographic differences between the PD and non-PD groups. Individuals in the PD group were older, with a median age of 65.8 years, whereas those in the non-PD group were 59.4 years, indicating a significant difference in age (P < 0.001). The sex distribution also varied significantly, with a greater proportion of males in the PD group (57.20%) than in the non-PD group (27.12%), where females were predominant (P < 0.001) (Fig. 2 ) . BMI was significantly lower in the PD group, with a median of 25.66, than in the non-PD group, with a median of 27.44 (P < 0.001). Employment status further highlighted disparities, as fewer individuals in the PD group were employed (31.93%) than in the non-PD group (55.37%), indicating another significant difference (P < 0.001). These findings suggest that age, sex, BMI, and employment status are key factors distinguishing the two groups. Similarly, univariate and bivariate analyses of key predictors revealed several significant differences between the PD and non-PD groups. Occupational history revealed that individuals in the PD group were more likely to have served in the military (15.48% vs. 7.34%, P = 0.01) and had greater exposure to pesticides through their jobs (17.14% vs. 7.34%, P < .001) (Fig. 3). Vigorous physical activity also differed significantly, with the PD group engaging in fewer hours per week, as a larger proportion reported less than 1 hour of vigorous activity weekly (29.97% vs. 41.24%, P = 0.03). The residential environment revealed that more individuals in the PD group than in the non-PD group lived in rural areas (10.38% vs. 3.95%, P = 0.01). Additionally, exposure to pesticide spray was greater in the PD group, with 42.41% reporting such exposure compared with 33.33% in the non-PD group (P = 0.03). These findings highlight occupational, environmental, and activity-related factors as key areas of difference between the two groups. Key Tables Table 3 provides the final logistic regression model. Of the 24 variables analyzed through backward adjusted-pseudo-R2 methodology for optimizing the model, 9 significant variables were selected as having the largest contribution to the model’s accuracy. Table 3 Final logistic regression model Predictor Variable Odds Ratio (OR) Lower CI (OR) Upper CI (OR) p value Age 39.22 14.17 108.51 < .001 BMI 0.15 0.06 0.37 < .001 Gender (Male) 3.48 2.33 5.18 < .001 Alcohol (Yes) 0.57 0.34 0.96 0.03 Education group (Higher) 0.69 0.46 1.05 0.08 Pesticide exposure at Job (Yes) 2.21 1.15 4.26 0.01 Vigorous Physical Activity (1–4 hrs) 1.75 1.14 2.69 0.01 Urban (small town) 0.64 0.41 0.98 0.04 Calcium Channel Blockers Hx 0.69 0.47 1.02 0.06 The logistic regression model examined factors influencing the likelihood of developing Parkinson’s disease, utilizing data from 1,198 participants and 21 predictors. The model achieved a pseudo R 2 of 0.2014, indicating a moderate ability to explain the variance in diagnosis likelihood. Older age emerged as the most significant risk factor for developing Parkinson’s disease, with individuals experiencing nearly 39-fold higher odds as they aged (OR = 39.37, p < .001). Conversely, a higher BMI was strongly protective, reducing the risk by 85% (OR = 0.15, p < .001). Compared with women, men were found to have a markedly increased risk, with odds 3.5 times greater (OR = 3.49, p < .001). Moreover, occupational exposure to pesticides doubled the likelihood of developing PD (2.2-fold increase, OR = 2.23, p = 0.01). Interestingly, regular alcohol consumption was associated with a 42% reduction in risk (OR = 0.58, p = 0.03), suggesting a potential protective effect. Surprisingly, engaging in 1–4 hours of vigorous physical activity weekly was linked to a 75% higher risk of PD (OR = 1.75, p = 0.010), suggesting complex interactions between activity and prodromal symptoms. Compared with living in rural areas, living in a small town was associated with a 36% lower risk of PD (OR = 0.64, p = 0.04), possibly reflecting protective environmental or lifestyle factors. Although higher educational attainment was associated with a 30% reduction in risk (OR = 0.70), the result was not statistically significant (p = 0.08), highlighting a potential trend that requires further study. Together, these findings provide a nuanced understanding of how demographic, lifestyle and environmental factors shape the risk of developing Parkinson’s disease. The total dataset was split into 70% and 30% to train and test the data for predictive modeling. After applying the synthetic minority oversampling technique (SMOTE) to balance the class distribution between PD and non-PD patients, the performance of both the logistic regression model and the random forest classifier model was evaluated for predicting Parkinson’s disease (PD) patients (class 1) and non-PD patients (class 0). Before SMOTE, logistic regression achieved high accuracy (88%) but struggled with predicting non-PD cases (class 0), with a low recall of 23%. However, it perfectly identified all PD cases (Class 1) with 100% recall. After SMOTE, the recall for non-PD cases (class 0) improved to 56%, but the precision decreased significantly to 0.29, and the overall accuracy decreased to 71%. In contrast, the random forest classifier achieved 81% accuracy after SMOTE, with the recall for non-PD cases (class 0) improving to 43% and the recall for PD cases (class 1) remaining high at 88%. While it performed well in predicting PD cases, its precision for non-PD cases (0.38) indicated challenges in correctly identifying non-PD individuals. Both models benefitted from SMOTE by improving the recall for non-PD cases, but logistic regression had a higher recall for Class 0, whereas the random forest classifier showed better overall accuracy and PD case (Class 1) prediction (Fig. 4). These results highlight the trade-off between improving class balance and maintaining strong performance in the majority class, with each model demonstrating different strengths post-SMOTE. Table 4 summarizes various classification reports before and after SMOTE. Table 4 Performance Metrics of the Classification Models Before and After the SMOTE Metric Logistic (Before SMOTE) Logistic (After SMOTE) RandomForestClassifier (After SMOTE) Accuracy 0.88 0.71 0.81 Precision (Class 0) 0.92 0.29 0.37 Precision (Class 1) 0.88 0.90 0.88 Recall (Class 0) 0.23 0.56 0.33 Recall (Class 1) 1.00 0.74 0.89 F1-Score (Class 0) 0.36 0.38 0.35 F1-Score (Class 1) 0.94 0.81 0.89 Macro Avg Precision 0.90 0.60 0.62 Macro Avg Recall 0.61 0.65 0.61 Macro Avg F1-Score 0.65 0.60 0.62 Weighted Avg Accuracy 0.89 0.80 0.80 Discussion Summary of Results The primary objective of this study was to explore the relationships between environmental risk factors and Parkinson's disease (PD) by examining demographic, socioeconomic, lifestyle, behavioral, health-related, and environmental factors in individuals with PD and those without PD. These findings confirm that environmental risk factors, including lifestyle choices, environmental exposures, and certain health-related factors, significantly differ between PD and non-PD individuals. Specifically, demographic factors such as older age and male sex were associated with a greater risk of developing PD. Socioeconomic factors such as unemployment or retirement were more common in the PD group, highlighting potential vulnerabilities tied to socioeconomic status. Behavioral factors such as lower levels of vigorous physical activity were also more prevalent among non-PD individuals, whereas higher activity levels in the PD group were associated with an increased risk of developing PD. This unexpected relationship may reflect reverse causality, where changes in behavior occur as a response to the disease rather than a contributing cause. These patterns align with findings from prior research that point to the complex interplay between age, sex, lifestyle, and environmental exposure in influencing PD risk. In line with our hypotheses, the results support the idea that lifestyle and behavioral factors, as well as occupational exposures (e.g., pesticide exposure), significantly influence the risk of developing PD. These results align with literature that points to environmental toxins (De Miranda et al., 2022 ), sex, and education (Al-Hakeem et al., 2024 ; Boina, 2022 ) as potential risk factors for PD, validating previous research that suggests a multifactorial etiology for this disease. In contrast to the findings of Chen et al. ( 2024 ), who suggested that high physical activity is associated with a lower risk of developing Parkinson's disease (PD), this study revealed that increased vigorous physical activity was linked to a greater risk of developing PD. Specifically, individuals engaging in 1–4 hours of vigorous activity per week, compared with those with less than 1 hour of activity, presented a significantly greater risk of developing PD. This finding was consistent across both bivariate analysis and logistic regression models, where higher levels of physical activity were associated with an increased risk of developing PD. These findings suggest that contrary to the protective effects observed in other studies, greater physical activity might not universally reduce PD risk and could be influenced by other contributing factors or confounding variables specific to our sample. Additionally, reverse causality may be a consideration—individuals who have already been diagnosed with PD may have modified their activity levels owing to their health condition, which could result in the observed higher levels of physical activity in the PD group. In this case, the outcome (PD diagnosis) could have influenced lifestyle choices rather than the influence of physical activity on the development of PD. Limitations One key limitation of this study is the reliance on self-reported data, which can introduce recall or social desirability biases. Additionally, the use of the mode to capture responses at different life stages (e.g., lifestyle, residence) might oversimplify the data. The mode represents the most frequent response, but it does not account for variations or changes over time. This may lead to a less nuanced understanding of how environmental and lifestyle factors, which can change over time, influence the development of Parkinson’s disease, potentially missing some important shifts or trends in participants' risk profiles. Future Directions Future research could expand on the predictive modeling employed in this study by incorporating more granular data on genetic factors, which may help better understand the complex interaction between genetic predisposition and environmental influences on PD. Additionally, longitudinal studies could be implemented to examine how exposure to certain environmental factors over time directly influences the onset of PD, providing clearer evidence for causal relationships. Implications and Importance This research underscores the critical role of environmental, lifestyle, and occupational factors in the development of Parkinson’s disease. Understanding these factors can inform nonclinical preventive measures, which are vital for reducing the incidence and impact of PD, especially as its prevalence continues to rise globally. The findings advocate for public health interventions that target lifestyle modifications and improve occupational safety to mitigate the environmental risks associated with PD. By addressing these modifiable risk factors early, we could reduce the burden of PD on individuals and healthcare systems worldwide. Therefore, this study contributes valuable insights for public health strategies, occupational safety policies, and future research into PD prevention. Declarations Author Contribution I'm the sole author of this paper Acknowledgments I would like to extend my heartfelt gratitude to my advisors, Dr. Katy Valentine & Dr. Stanley Nwoji, for their invaluable guidance and support throughout the completion of this thesis. Their expertise and encouragement have been instrumental in this academic journey. Additionally, I acknowledge the use of data from the Fox Insight Study. The Fox Insight Study (FI) is funded by The Michael J. Fox Foundation for Parkinson’s Research. I am deeply grateful to Parkinson’s community for their participation in this study, which made this research possible. The data used in the preparation of this study were obtained from the Fox Insight database (https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp) on 09/11/2024. For up-to-date information on the study, visit https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp. I declare that I have no conflicts of interest related to this research. I am grateful to my peers at Harrisburg University for their encouragement and camaraderie during this process. I am also thankful to the HU library staff for their constructive feedback and support. Finally, I would like to thank my family and friends for their unwavering support and patience, which has been a constant source of strength. Data Availability Data used in the preparation of this thesis were obtained from the Fox Insight database (https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp). The analysis, derived datasets, and associated code developed specifically for this study are available from the corresponding author upon reasonable request, subject to compliance with data-sharing agreements and ethical guidelines. References Lee, J., Chung, M., Kim, E., & Yoo, J.‐H. (2024). Impact of caregiver relationship on self‐care in patients with Parkinson's disease: A cross‐sectional study using Riegel's theory of self‐care of chronic illness. Journal of Clinical Nursing (John Wiley & Sons, Inc.), 33 (3), 1036-1047. doi: https://doi.org/10.1111/jocn.16905 McDonald, T., Ronksley, P., Cook, L., Patel, A., Seidel, J., Lethebe, B., & Green, L. (2024). The Impact of Primary Care Clinic and Family Physician Continuity on Patient Health Outcomes: A Retrospective Analysis From Alberta, Canada. 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Brain and Behavior, 12 (8), e2690. doi:https://doi.org/10.1002/brb3.2690 Machado-Fragua, M. D., Dugravot, A., Dumurgier, J., Kivimaki, M., Sommerlad, A., & Landre, B. (2021). Comparison of the predictive accuracy of multiple definitions of cognitive impairment for incident dementia: a 20-year follow-up of the Whitehall II cohort study. The Lancet Healty Logevity, 2 (7), E407-E416. doi:https://doi.org/10.1016/S2666-7568(21)00117-3 Maggi, G., Giacobbe, C., Vitale, C., Amboni, M., Obeso, I., & Santangelo, G. (2024). Theory of mind in mild cognitive impairment and Parkinson's disease: The role of memory impairment. Cognitive, affective & behavioral neuroscience, 24 (1), 156–170. doi: 10.3758/s13415-023-01142-z Müller-Nedebock, A. C., Dekker, M. C., Farrer, M. J., Hattori, N., Lim, S.-Y., Mellick, G. D., . . . Bardien, S. (2023). Different pieces of the same puzzle: a multifaceted perspective on the complex biological basis of Parkinson’s disease. npj Parkinson's Disease, 9 , 110. doi:https://doi.org/10.1038/s41531-023-00535-8 Munoz-Pinto, M. F., Empadinhas, N., & Cardoso, S. M. (2021). The neuromicrobiology of Parkinson’s disease: a unifying theory. Aging Research Reviews, 70 , 101396. doi:https://doi.org/10.1016/j.arr.2021.101396 National Health Services. (2022, November 3). Parkinson's disease. Retrieved from NHS: https://www.nhs.uk/conditions/parkinsons-disease/treatment/#:~:text=There's%20currently%20no%20cure%20for, supportive%20therapies%2C%20 such%20as%20physiotherapy National Institute on Aging. (2022, April 14). Parkinson’s Disease: Causes, Symptoms, and Treatments. Retrieved from National Institute on Aging: https://www.nia.nih.gov/health/parkinsons-disease/parkinsons-disease-causes-symptoms-and-treatments Newhouse, J. P. (2021). An ounce of prevention. Journal of Economic Perspectives. 35 (2), 101-118. Retrieved from https://www.jstor.org/stable/27008031?seq=3 NINDS. (2023, January 30). Parkinson's Disease: Challenges, Progress, and Promise. Retrieved from National Institute of Neurological Disorders and Stroke(NINDS): https://www.ninds.nih.gov/current-research/focus-disorders/parkinsons-disease-research/parkinsons-disease-challenges-progress-and-promise#toc-conclusion Organization, W. H. (2023). 2023 emerging technologies and scientific innovations: a global public health perspective. Retrieved from https://iris.who.int/bitstream/handle/10665/367116/WHO-SCI-RFH-2023.05-eng.pdf?sequence=1 Ou, Z., Pan, J., Tang , S., Duan , D., Yu, D., Nong , H., & Wang, Z. (2021). Global Trends in the Incidence, Prevalence, and Years Lived With Disability of Parkinson's Disease in 204 Countries/Territories From 1990 to 2019. Front. Public Health, 9 , 776847. doi:doi: 10.3389/fpubh.2021.776847 Poulia, K. A. (2024). Nourishing Neurons: Exploring the Role of Plant-Based Diets in Parkinson’s Disease Prevention. Kompass Nutrition & Dietetics, 4 (1), 23–24. Retrieved from https://doi.org/10.1159/000538286 Qasim, M. M., Daghriri, A. A., Alanazi, O. A., AlOmari, L. I., Alzibali, K. F., Alrezqi, W. A., . . . Refai, H. M. (2023). Evaluation of Knowledge and Attitudes of the Population of Tabuk City Regarding Parkinson's Disease: A Cross-Sectional Study. Cureus, 15 (10), e46442. doi:https://doi.org/10.7759/cureus.46442 Rajan, S., & Kaas, B. (2022). Parkinson's Disease: Risk Factor Modification and Prevention. Seminars in Neurology, 42 (05), 626-638. doi:10.1055/s-0042-1758780 Sarb, O. F., Sarb, A. D., Iacobescu, M., Vlad, I. M., Milaciu, M. V., Ciurmarnean, L., . . . Tantau, A. I. (2024). From Gut to Brain: Uncovering Potential Serum Biomarkers Connecting Inflammatory Bowel Diseases to Neurodegenerative Diseases. International Journal of Molecular Sciences , 25 (11), 5676. doi:https://doi.org/10.3390/ijms25115676 Schiess, N., Cataldi, R., Okun , M. S., & et al. (2022). Six Action Steps to Address Global Disparities in Parkinson Disease: A World Health Organization Priority. JAMA Neurol., 79 (9), 929-936. doi:10.1001/jamaneurol.2022.1783 Scorza, F. A., Almeida, A. G., Scorza, C. A., & Finsterer, J. (2021). Prevention of Parkinson’s disease-related sudden death. Clinics, 76 , e3266. doi:https://doi.org/10.6061/clinics/2021/e3266 Silva, A., Oliveria, R., Diogenes, G., Aguiar, M., Sallem, C., Lima, M., . . . Mendonça, L. (2023). Premotor, nonmotor and motor symptoms of Parkinson's Disease: A new clinical state of the art. Aging Research Reviews, 84 , 101834. doi:https://doi.org/10.1016/j.arr.2022.101834 Smolensky, L., Amondikar, N., Crawford, K., Neu, S., Kopil, C. M., Daeschler, M., & Riley, L. (2020). Fox Insight collects online, longitudinal patient-reported outcomes and genetic data on Parkinson’s disease. Scientific Data, 7 (67), 8228. doi:https://doi.org/10.1038/s41597-020-0401-2 The Lancet Psychiatry. (2022, August). Prevention is better than cure. Prevention is better than cure, 9 (8), p. 601. doi:https://doi.org/10.1016/S2215-0366(22)00238-3 Thrope, K. E. (2022). Racial trends in clinical preventive services use, chronic disease prevalence, and lack of insurance before and after the Affordable Care Act. The American journal of managed care, 28 (4), e126–e131. doi:https://doi.org/10.37765/ajmc.2022.88865 Vellata, C., Belli , S., Balsamo, F., Giordano, A., Colombo, R., & Maggioni , G. (2021). Effectiveness of Telerehabilitation on Motor Impairments, Nonmotor Symptoms and Compliance in Patients With Parkinson's Disease: A Systematic Review. Frontiers in neurology, 12 , 627999. doi: https://doi.org/10.3389/fneur.2021.627999 Williams, D. (2024). Why so slow? Models of parkinsonian bradykinesia. Nature Reviews Neurosience, 25 , 573-586. doi:https://doi.org/10.1038/s41583-024-00830-0 Ying, A. C., & Vasanthi, R. K. (2022). A survey of exercise beliefs among people with Parkinson’s disease. Physiotherapy Quarterly, 30 (3), 57–63. doi:https://doi.org/10.5114/pq.2021.103556 Additional Declarations No competing interests reported. Supplementary Files Appendices.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5679320","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":418655784,"identity":"8f9f292b-4347-4742-b468-3d6e0426dea1","order_by":0,"name":"Niharika Namburi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYHACNiCWYGCTAFIJBxjk+EFiCQUkaDGWbAAxDAhqAesCggMMiRsOgBh4tJizn057zLvHIo9PuvnZhwdn7BI3n1+d+OGBAYM8v9gBrFose3K3G/M8kyhmkzlmPCPhRrLxthtvN0sAHWY4c3YCVi0GB3K3SfMckEhsk0gwZkj4wCy77cbZDSAtCQa3cWg5/xamJf0zUEs94+YZZzf/wKvlBtyWHKAtNw4rbuDv3YbflhtvtxvOgWgpZkg4c9xY4gbvNosEAwncfjmfu+3BmwN1ifNnpG9m/HGsWo6//+zmmz8qbOT5pbFrwQIkwColiFUOAvwHSFE9CkbBKBgFIwAAACR5ZScaYsArAAAAAElFTkSuQmCC","orcid":"","institution":"Harrisburg University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Niharika","middleName":"","lastName":"Namburi","suffix":""}],"badges":[],"createdAt":"2024-12-19 20:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5679320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5679320/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77313317,"identity":"78f916f6-ee94-499a-9ee1-5cecf295b22e","added_by":"auto","created_at":"2025-02-27 10:16:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePair Plot of Age and BMI by PD Diagnosis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eAge and BMI are skewed.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5679320/v1/19731cb07f9acaa045b33be2.png"},{"id":77313318,"identity":"006e3503-3a9e-4c85-9a3d-007f4e415331","added_by":"auto","created_at":"2025-02-27 10:16:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of males and females by PD diagnosis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5679320/v1/959fe90a8ed6e6ad79a3e027.png"},{"id":77314713,"identity":"275a469f-2b1f-4e44-843c-39aa9dcb2d0e","added_by":"auto","created_at":"2025-02-27 10:24:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe number of PD and non-PD individuals in the pesticide exposure group (yes/no)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5679320/v1/002e1a568c017820bc89e8fc.png"},{"id":77313321,"identity":"95e8c99d-666c-4beb-8c44-837ed74ffcd6","added_by":"auto","created_at":"2025-02-27 10:16:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConfusion Matrices for the Logistic Regression and Random Forest Models after SMOTE\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5679320/v1/b9aa996af173c640b9ba3190.png"},{"id":77315047,"identity":"aa8d5625-299f-41b5-b4b9-be4e687f0826","added_by":"auto","created_at":"2025-02-27 10:32:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1410890,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5679320/v1/3b290fe8-bdfe-4fd4-9aee-a87e2eb6089a.pdf"},{"id":77313319,"identity":"b76ceff4-595b-4c38-a641-759b083b57f6","added_by":"auto","created_at":"2025-02-27 10:16:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":136328,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-5679320/v1/444a4f761b953c7ff1c8f292.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nonclinical Preventive Measures of Parkinson's Disease (PD): Identifying Key Lifestyle, Demographic, and Environmental Factors","fulltext":[{"header":"Literature Review","content":"\u003cp\u003eParkinson’s disease (PD) is the second most recurring brain aging disease, with a history of 200 years, after James Parkinson’s disease (Kulcsarova et al., 2024; Deliz et al., 2024). It is the second most prevalent neurodegenerative disease globally, following Alzheimer's disease, and it is estimated to impact approximately 1% of individuals over 60 years of age and may impact approximately 5% of individuals over 85 years of age (Choo et al., 2020; Crooks et al., 2023; Czarnik et al., 2024). However, it is estimated that 5–10% of people with PD are diagnosed before the age of 50. Approximately 15 to 25% of individuals with Parkinson’s disease are estimated to have a family member who also has this condition. (National Institute of Neurological Disorders and Stroke, 2023). The pathological definition is “the result of selective degeneration of dopaminergic neurons in the substantia nigra, which causes a decreased level of dopamine in the striatum and leads to abnormal motor control” (Chia et al, 2020). Other definitions include PD as pathological misfolding of α-synuclein aggregates, also known as Lewy body deposits, that impact the central, peripheral, and enteric nervous systems (Klann et al., 2022). This enteric nervous system underlies the “brain‒gut” axis, a bidirectional pathway that emphasizes the role of inflammation and the microbiome in PD neurodegeneration (Klann et al., 2022). Clinically, Parkinson's disease is a neurodegenerative disorder that primarily affects the motor system, causing symptoms such as tremors, rigidity, bradykinesia, and postural instability. In addition to the well-known motor symptoms of Parkinson's disease, several nonmotor symptoms can significantly impact the health-related quality of life (HRQoL) of individuals (Bloem et al., 2021; Kulcsarova et al., 2024; Deliz et al., 2024).\u003c/p\u003e\n\u003cp\u003eThese nonmotor symptoms can include depression, anxiety, cognitive changes, and sleep disturbances (Bougea, 2024). The World Health Organization (WHO) defines HRQoL as “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns. HRQoL assessment includes motor and physical skills, mental health, somatic perception, and socioeconomic conditions” (Crispino et al, 2021). Despite many studies on the etiology of PD, identifying the cause in most patients is difficult; however, various genetic etiologies have been identified.\u003c/p\u003e\n\u003cp\u003eThe etiology of PD is multifactorial, with variations in geography, age, sex, genetics, and environmental factors (Deliz et al, 2024, Berg et al., 2022). In the last decade, significant advancements in genetics coupled with extensive epidemiological studies have led to comprehensive knowledge of the genetic, behavioral, and environmental influences involved in the development and progression of Parkinson’s disease. Bolem and Kelin (2021) mentioned that treatment goals vary for each person, emphasizing the need for personalized management. At present, no therapy can slow or arrest the progression of Parkinson's disease.\u003c/p\u003e\n\u003cp\u003eOwing to scientific and technological advancements in the medical field, the average life span of humans has increased globally. Simultaneously, there has been an increase in chronic diseases in the aging population. PD is a rising trend highly associated with the aging population. Ou et al. (2021) conducted a global study related to PD in 204 countries from 1990--2019. This study highlighted occupational factors, unhealthy lifestyles, and environmental pollution, indicating that more effective strategies are needed to address this global health challenge.\u003c/p\u003e\n\u003cp\u003eWhile the exact cause of Parkinson's disease is not fully understood, researchers have identified a variety of risk factors and potential preventive strategies that may help reduce the risk of developing this debilitating condition (Müller-Nedebock et al., 2023). This paper aims to focus on preventive strategies and how they can be advocated to patients and their families so that they become familiar with major diseases such as diabetes, breast cancer, and HIV/AIDS.\u003c/p\u003e\n\u003cp\u003eCrooks et al. (2023) conducted a scooping review on public perceptions and awareness of PD. They reported that there was a significant lack of understanding of the disease among the public and that few educational resources were available. The study concluded that public perceptions and awareness are crucial for early diagnosis, effective management, and improving overall quality of life. This can influence public support for research, funding, and legislative/policy decisions.\u003c/p\u003e\n\u003ch2 id=\"_Toc184156988\"\u003eParkinson's Disease Preventive Measures\u003c/h2\u003e\n\u003cp\u003eIn the current technology-driven world, we believe that spreading information is relatively easy. However, that is not the case in educating the public regarding Parkinson’s disease. A considerable amount of noise is needed in public awareness of PD, such as in the case of breast cancer. Charlotte Haley, a frustrated 68-year-old woman, led the pink ribbon movement to bring awareness among the public to attract the attention of legislators (Dorsey et al., 2020; De Miranda et al., 2022). Similar movement is necessary in the case of PD. Scholars have estimated that the number of people affected by PD has grown exponentially since 1990. It ranged from 2.5 million in 1990 to 6.2 million in 2015 and is expected to double to 12.9 million by 2040 (De Miranda et al., 2022). Males are affected by Parkinson's disease almost 1.6 times more than females are affected, with rates of 61.21 per 100,000 for males and 37.55 per 100,000 for females (Boina, 2022). Many public and private funders have poured their resources into finding a cure for PD, especially with respect to genetic factors (both inherited and idiopathic PD). However, microscopic studies on nongenetic factors and preventive measures have been conducted (De Miranda et al., 2022). De Miranda et al. (2022) studied mainly environmental contaminants, such as pesticides, metals, and industrial chemicals. They opined that such contamination increases the risk of developing PD. According to their study, approximately 27% of PD cases are heritable, and exogenous factors largely influence the remaining percentage. They developed a “PD prevention agenda” to highlight primary prevention along the lines of preclinical/basic research and clinical/translational research.\u003c/p\u003e\n\u003cp\u003eRajan and Kaas (2022) reported that physical activity, an early active lifestyle, high serum uric acid, caffeine consumption, tobacco exposure, the use of nonsteroidal anti-inflammatory drugs, and the use of calcium channel blockers along with a Mediterranean diet reduce the risk of PD. These are considered protective factors for PD. Additionally, evidence has shown that caffeine intake combined with physical activity can act as primary prevention and disease-modifying strategies in PD patients (Belvisi, et al., 2020). In contrast, the combination of hereditary factors such as pesticide exposure, farming, high dairy consumption, and head trauma injuries are known to increase the risk of PD (Belvisi, et al., 2020; Rajan \u0026amp; Kaas, 2022). Patients with Parkinson's disease who arrive at the emergency department due to injuries tend to have generally poor health and a high number of underlying conditions. This allows the identification of comorbidities along with demographic and socioeconomic factors as potential risk factors for PD (Al-Hakeem et al., 2024). Individualized care could help identify risk factors and prevent them from improving quality of life.\u003c/p\u003e\n\u003cp\u003eChen et al. (2024) prospectively investigated the associations of PD with physical activity, sleep patterns, and the combination of these two risk factors. Both high physical activity and good sleep are associated with a lower risk of developing PD. Additionally, high physical activity and poor sleep are associated with lower risk. These results were consistent among both sexes and across different age groups. Interventions involving the combination of physical activity and ideal sleep patterns would be promising prevention strategies.\u003c/p\u003e\n\u003cp\u003eCzarnik et al. (2024) studied the effects of the brain‒gut axis and the special role of diet in the occurrence of PD. The underlying emphasis is reducing inflammation by maintaining a healthy diet. In particular, the Mediterranean diet consists of plant products, olive oil, fish, and seafood, with less red meat. This study demonstrated that modifications to gut microbes can have a positive impact on neuropsychiatric health. Poulia (2024) conducted a prospective study among 126283 participants from the UK Biobank cohort to explore the role of plant-based diets in the prevention of PD. The results revealed that greater consumption of vegetables, nuts, and tea was linked to a reduced risk of PD by 28%, 31%, and 25%, respectively. Klann et al. (2022) reviewed the literature to shed light on the relationship between the gut microbiota and PD. This study revealed that providing pre- and probiotic supplementation during adolescence improved resilience toward neurodegenerative disorders by reducing inflammation and promoting neurogenesis.\u003c/p\u003e\n\u003cp\u003eKlann et al. (2022) proposed that evaluating the composition of the gut microbiota could help in detecting the early onset of PD. Balakrishnan et al. (2021) reported that oxidative stress and neuroinflammation are significant factors responsible for PD progression. This study revealed that naturally derived phytochemicals and their derivatives are protective factors with no adverse effects when consumed. For example, phytochemicals such as chrysin are found in honey, vanillin is found in vanilla beans, and caffeic acid is found in coffee, spinach, tomatoes, and berries. Using natural phytochemicals as a part of the diet, supplements or novel therapeutic interventions would help prevent PD or slow its progression.\u003c/p\u003e\n\u003ch2 id=\"_Toc184156989\"\u003eClinical Preventive Measures\u003c/h2\u003e\n\u003cp\u003eAt present, there is no known cure for PD (National Institute on Aging, 2022; National Health Services, 2022; Scorza et al., 2021). However, treating disease symptoms predominantly focuses on the dopaminergic pathway via levodopa drugs, deep brain stimulation (DBS) procedures, and stem cell transplants (Scorza et al., 2021; Bjørklund et al., 2020; Crooks, et al., 2023). Some novel drug targets, including lipid peroxidation, protein oxidation, DNA damage, and mitochondria, are still in progress (Bjørklund et al., 2020). The clinical aspects are beyond the scope of this study.\u003c/p\u003e\n\u003ch2 id=\"_Toc184156990\"\u003eNonclinical Preventive Measures\u003c/h2\u003e\n\u003cp\u003e“Prevention is better than cure” (The Lancet Psychiatry, 2022). The modern version of the Hippocratic Oath, which is the most popular among medical graduates, says, “I will prevent disease whenever I can, for prevention is preferable to cure” (Newhouse, 2021). In health care, prevention is categorized into three main categories: primary, secondary, and tertiary (AbdulRaheem, 2023). Primary prevention focuses on preventing diseases or injuries from occurring first. This paper focuses on the primary prevention component of PD. For example, individuals should be educated regarding healthy eating and safe habits. Secondary prevention focuses on addressing the slow progression of the disease or reducing the impact of disease or injury that has already occurred. For example, modified occupational conditions where individuals can return safely to their jobs. Finally, tertiary prevention focuses on reducing the long-term impact of an illness or injury that is already present. This includes programs that involve support groups to share strategies for living with chronic illnesses (AbdulRaheem, 2023). At the community and population levels, telehealth and genetic sequencing could help marginalized groups attend preventive programs and ultimately aim for the early identification of modifiable risk factors and timely implementation of effective interventions (Lau, 2023). Vellata et al. (2021) systematically reviewed the effectiveness of telemedicine and telerehabilitation with PD patients. This study concluded that telerehabilitation and telemedicine are feasible for PD patients who have positive changes in their preassessment perceptions and high satisfaction levels in managing their nonmotor and motor-related symptoms and overall quality of life. These options help minimize barriers such as distance, time, and cost. Kwok et al. (2023) conducted a random control trial of mindfulness meditation exercises in PD patients, and the results showed that mindfulness meditation proved to be a promising strategy for managing depressive symptoms and improving cognitive performance among mild to moderate PD patients. Modifiable risk factors may impact cognitive outcomes in PD patients, and evidence has shown that aerobic exercise improves cognition in PD patients (Carlisle et al., 2023).\u003c/p\u003e\n\u003cp\u003eMobile health (mHelaht) technology can collect both clinical and nonclinical information and exchange that information with existing health informatics systems such as electronic health records (Bouça-Machado et al., 2021). One of these advantages is continuous monitoring, which can also help empower patients to self-manage PD or practice healthy lifestyles such as walking, sleep patterns, and self-report questionnaires to track progress and provide an interactive platform for both healthcare professionals and patients and caregivers to deliver personalized care (Bouça-Machado, et al., 2021). Choo et al. (2020) conducted a survey using the Knowledge and Perception of Parkinson’s Disease Questionnaire (KPPDQ) at a university hospital neurology clinic and reported that there are knowledge gaps, misperceptions and perspectives on PD. Additionally, research on PD-related stigma remains scarce, and further research is recommended to understand the magnitude of knowledge gaps and perspectives in individual and community areas (Choo et al., 2020).\u003c/p\u003e\n\u003ch2\u003eFactors affecting the study (independent variables)\u003c/h2\u003e\n\u003cp\u003eAge, sex, race, BMI, geographical location, socioeconomic, diet, lifestyle, physical activity, social engagement, mental stimulation, sleep patterns, daytime sleepiness, fatigue, anxiety, depression, comorbidities (such as vascular risk, hypertension, diabetes, coronary heart disease, hypercholesterolemia), medications, technology acceptance (wearables, sensors, smartphone applications), occupation, urban or rural living, pesticide exposure (such as amphetamine or methamphetamine, paraquat, and chlorpyrifos), industrial solvents (such as trichloroethylene), drinking water contamination, living near industrial areas, air pollution, head injuries/traumatic brain injuries, lack of awareness, education, and stigma around neurodegenerative disorders are some of the significant factors mentioned in various studies. Most of the studies used demographic, socioeconomic, and lifestyle variables (such as age, sex, ethnicity, comorbidities, education, healthy lifestyle, and BMI) to determine the associations with the development of PD in the future or to identify PD-risk individuals. Chen et al. (2024) reported that participants who developed PD during the follow-up period were typically older; male; nonsmokers; and users of hypertension medication, as well as those with diabetes. Compared with those who exercised less, those who exercised more were often men and White, had a lower BMI, did not smoke, spent less time sitting, ate healthier, and had fewer chronic illnesses such as high blood pressure and diabetes. A similar trend followed in terms of sleep patterns and daytime sleepiness. Individuals with healthy sleep characteristics, such as larks (morning chronotype), 7–8 hr/day sleep, no insomnia, no snoring, and no frequent daytime sleepiness, had a low risk of PD development. Participants with high total physical activity (including work, transportation, chores, gardening, and leisure) had a 27% lower risk of developing Parkinson's disease than did those with low physical activity, even after adjusting for various factors (Chen, et al., 2024). Boina (2022) reported that functional and aerobic activities can strongly slow the progression of Parkinson’s disease. Choo et al. (2020) reported that most caregivers and PD patients are unable to recognize nonmotor symptoms such as pain, a reduced sense of smell, urinary problems, and visual hallucinations in the early stages. This study highlighted the major misconception that PD has curative treatment due to stem cell procedure advertisements. Crooks et al. (2023), in their scoping review, identified a lack of awareness among the public, scarce education resource availability, and stigma around neurodegenerative disorders, specifically PD, and found associations between PD and depression, isolation, and loss of independence. Chevinsky et al. (2024) reported that diet has a significant effect on reducing the risk of PD. Various factors, such as age, toxic substances, oxidative stress, alcohol, physical activity, medications, or drugs, disrupt the gut microbiome, and an unhealthy lifestyle is associated with increased inflammation, which in turn increases the risk of PD. De Miranda et al. (2022) reported that sex/gender, as a biological variable in toxicant exposure, and PD are more prevalent in men than in women because men are more exposed to risk factors such as pesticide applicators and factory workers. However, one study from Japan showed that PD is more prevalent in women, possibly because Japan has more female farmers (De Miranda et al., 2022). This evidence shows that geographical and cultural factors may play a role in PD incidence. This study emphasized that combined exposure to solvents, pesticides, metals, and other industrial byproducts must be considered risk factors for PD, as the additive or synergistic effects of these compounds influence their toxicokinetics and ultimately their combined neurotoxicity. The Cures Act in U.S. law emphasizes that the use of mobile devices, wearable technology, and biosensors offers the potential to more effectively involve individuals in managing their healthcare (De Miranda et al., 2022; Bouça-Machado et al., 2021).\u003c/p\u003e\n\u003ch2 id=\"_Toc184156992\"\u003eGaps in the Literature\u003c/h2\u003e\n\u003cp\u003eDe Miranda et al. (2022) failed to clarify exogenous factors (nongenetic) such as pathogenetic infection, head trauma, diet, pharmaceutical, supplement, drug use, and other physiological stressors interact with each other or how individuals become more at risk of developing PD if more than one factor is present. There are few gaps in assessments of the chronic consumption of low-level pesticide-laden foods and other commonly used industrial chemicals, such as dry cleaners, mechanics, computer chip manufacturers, etc. More interdisciplinary research involving the combination of multiple factors and effective preventive strategies to reduce exposure to environmental toxins is needed. Chen et al. (2024) focused on physical activity and sleep pattern combinations; however, other lifestyle factors and their interactions have not been discussed thoroughly. More research is needed to establish dietary interventions and specific mechanisms, as the relationship between diet and PD is complex and varies from person to person (Poulia, 2024). Additional research on human subjects is needed to yield consistent results and significant findings for a better understanding of the role of the microbiome‒gut‒brain axis in PD. Further studies are needed to perform a comprehensive evaluation of the role of natural phytochemicals (such as chrysin and caffeic acid) in neuroprotective or therapeutic activities in PD (Balakrishnan et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe Kwok et al. (2023) study control group did not receive any intervention, which limits the ability of mindfulness, and the lack of long-term follow-up fails to capture the lasting and consistent effect of mindfulness meditation in PD patients. Additionally, the sample size is relatively small, limiting the generalizability of the results. User experience (of patients and caregivers) with mobile-based monitoring systems has not been explored in depth, and there is a need for larger, diverse research studies to confirm the effectiveness of mobile-based monitoring on quality of life and patient outcomes. We need population-based prospective studies to understand disease incidence trends over time and to compare these trends with environmental factors. This includes assessing the air, water, and food that people consume and making environmental testing records freely available to researchers (De Miranda et al., 2022). The literature on public awareness is very limited, especially since public perceptions of PD vary across different cultures and regions. Limited research has been conducted on PD awareness campaigns, and further research incorporating patient perspectives and caregivers in public awareness campaigns is needed.\u003c/p\u003e\n\u003ch2 id=\"_Toc184156993\"\u003eReview of Existing Methodologies\u003c/h2\u003e\n\u003cp\u003eThe literature review encompasses a wide range of research methodologies related to PD, such as systematic and critical reviews, cross-sectional studies, retrospective analyses, cohort studies, quantitative studies, randomized controlled trials, and specific topic reviews.\u003c/p\u003e\n\u003cp\u003eDe Miranda et al. (2022) performed a systematic and critical review to identify the environmental factors contributing to Parkinson’s disease (PD). They analyzed global epidemiological data on contaminant emissions to estimate PD incidence. Preventive strategies such as mindfulness meditation and physical exercise can help individuals manage PD symptoms by reducing stress, improving mental health, and improving overall well-being. Similarly, Ahern et al. (2024), Belvisi et al. (2020), and Bjorklund et al. (2020) conducted systematic and critical reviews to evaluate behavioral change interventions, modifiable risk factors, and preventive strategies/treatments. Cohort studies such as Chen et al. (2024) explored correlations between physical activity, sleep patterns, and disease incidence. Qualitative studies, such as Chen et al. (2023), identify barriers to and facilitators of palliative care delivery. Randomized clinical trials, such as that of Kwok et al. (2023), compare mindfulness meditation and exercise interventions. Cross-sectional studies, such as that of Lee et al. (2024), have examined the relationships between caregiver involvement and self-care in patients. McDonald et al. (2024) and Al-Hakeem et al. (2024) conducted retrospective analyses to investigate the impacts of primary care continuity and comorbid conditions on health outcomes and emergency visits, respectively. Furthermore, reviews on topics such as the gut‒brain axis (Klann et al., 2022) and public perceptions of Parkinson's disease (Crooks et al., 2023) provide comprehensive insights into various factors influencing disease management and prevention.\u003c/p\u003e\n\u003ch2 id=\"_Toc184156994\"\u003eGaps in Methodologies\u003c/h2\u003e\n\u003cp\u003eDe Miranda et al. (2022) could have used more comprehensive data from diverse populations; this study focused on only sex in their PD prevention agenda. The incorporation of longitudinal studies could reveal causality between environmental exposure and PD. Using geospatial data would help to track and improve the accuracy of identifying specific environmental risks (De Miranda et al., 2022). Chen et al. (2024) conducted a prospective cohort study using the UK Biobank and focused mainly on the risk factors for physical activity and sleep patterns. It is geographically constrained, and the results are not generalizable. As these study data are self-reported, there might be issues such as recall bias and inaccuracies. Studies that focus on diet components are mostly qualitative and need to be conducted thoroughly to determine the effects of interventions. In regard to dietary interventions, even though some prospective studies have been conducted, they are mostly confined to one geographic location, which makes it difficult to generalize the results. Age, sex, race, food habits, allergies, different countries' diets, geographical locations, etc., make implementing specific mechanisms somewhat difficult (Chen et al., 2024; De Miranda et al., 2022; Bhidayasiri 2024).\u003c/p\u003e\n\u003ch2 id=\"_Toc184156995\"\u003eReview of Existing Theories\u003c/h2\u003e\n\u003cp\u003eMost of the articles in the literature review presented empirical findings, reviewed literature, or discussed practical applications without explicitly proposing a new theory. The following are some of the highlights from those articles that underscore the factors affecting this study:\u003c/p\u003e\n\u003cp\u003eChen et al. (2024) suggested that existing theories related to the neuroprotective effects of physical activity and the role of sleep in neurodegeneration provide empirical support for both. This highlights the combined benefit of these lifestyle factors in reducing PD risk, suggesting that interventions targeting physical activity and sleep quality could be promising strategies for PD prevention. Further research is needed to explore the underlying mechanisms, long-term impacts, and effectiveness of such interventions across diverse populations. Prevention strategies involving a diet with anti-inflammatory and neurotransmitter components could reduce the risk of PD. A Mediterranean diet, such as simple fiber consumption, impacts PD progression (Czarnik, et al., 2024). Cognitive decline in PD patients could be influenced by modifiable risk factors such as physical activity, diet, mental stimulation, and social engagement (Carlisle, Medina, \u0026amp; Holden, 2023). Bouça-Machado et al. (2021) suggested that mobile-based systems can effectively monitor Parkinson's disease (PD) symptoms remotely, providing continuous and real-time data that can enhance disease management and patient care.\u003c/p\u003e\n\u003cp\u003eSome general existing theories about Parkinson’s disease include behavioral interventions, ethical constructs, cognitive ability, prevention, and management of falls/fear of falling in PD patients, etiology, and overall ways to improve quality of life.\u003c/p\u003e\n\u003cp\u003eAhern et al.’s (2024) systemic review is based on the theoretical domain framework (TDF) to understand and implement behavior change interventions in Parkinson’s disease patients. This framework consists of 5 key domains, namely, “behavioral regulation,” “beliefs about capabilities,” “social influences,” “reinforcement,” and “goals,” to help with disease management. The authors emphasized that most interventions were multicomponent and involved education, behavioral techniques, and support groups. The authors use physical activity and exercise variables to explain the framework and how it improves health in PD patients. The PD patient group tends to be 30% less active than the control group, i.e., health age-matched (Ahern et al., 2024). This review also highlighted that depression, apathy, and similar nonmotor features are barriers to physical activity, such as exercise, in PD patients. To motivate and improve overall health in PD patients, especially exercise self-efficacy and adherence, the authors underscore the identification of behavioral change interventions that, in turn, lead to improved strength, fitness, and quality of life (QoL) and how these interventions map to TDFs. It includes education programs such as brochures, weekly lectures on suitable exercises, and overcoming barriers; behavioral techniques such as decision-making, problem-solving, and identifying potential barriers; and technology usage involving activity trackers, virtual coaches, tailored content to a person’s needs, and motivation. Support groups through peer-group online sessions and resource availability in the environmental context help in action planning and social influences.\u003c/p\u003e\n\u003cp\u003eMaggi et al. (2024) focus on the concept of the “Theory of Mind” (ToM), which refers to the ability to understand and attribute mental states such as beliefs, intentions, desires, and emotions to oneself and others. This article explores how cognitive impairments, especially memory problems, affect this ability in individuals with mild cognitive impairment (MCI) and Parkinson's disease (PD). This study highlights how memory impairment impacts ToM in these populations, suggesting that memory difficulties can significantly influence social cognition and interpersonal interactions in people with MCI and PD.\u003c/p\u003e\n\u003cp\u003eBasas and Gozum (2023) proposed a culture of encounters (CoE). This theory emphasizes the importance of compassionate care in healthcare, particularly for individuals with Parkinson's disease. It highlights the ethical aspect of healthcare by creating a culture of encounters between patients and providers characterized by empathy, understanding, and mutual respect. The theory aims to improve the overall care experience and support the emotional and psychological well-being of patients with Parkinson's disease. It primarily advocated for a holistic, patient-centric approach to care.\u003c/p\u003e\n\u003cp\u003eLee et al. (2024) used Riegel's theory of self-care for chronic illness to highlight the complex and often contradictory interactions between different developmental factors. This study underscores the role of individuals in actively managing their PD through self-care practices. This study examines how the relationship between PD patients and their caregivers influences patients’ ability to engage in self-care. These findings underscore that supportive and positive caregiver relationships significantly improve patients' self-care behaviors, leading to better disease management and overall well-being (Lee et al., 2024).\u003c/p\u003e\n\u003cp\u003eChen et al. (2023) conducted semistructured interviews via the social ecological model (SEM) framework to identify the barriers and facilitators influencing palliative care for PD patients and their caregivers living in China. The World Health Organization (WHO) defines palliative care (PC) as “an approach (through the prevention and relief of suffering, early identification, impeccable assessment, and treatment) to improve the quality of life of patients and their families, those who are facing the pain and other physical, psychological and spiritual problems associated with a life-threatening illness” (Chen et al., 2023). Palliative care facilitators are identified at the individual (PD patients and caregivers' needs; providers’ PC knowledge), interpersonal (social support), organizational (encouraging connectors such as nurses), community, cultural, and policy levels. Barriers include PC misconceptions economically at the individual level; miscommunication at the interpersonal level; a lack of access to PC resources; death; and ethical dilemmas surrounding PD at the community, culture, and policy levels.\u003c/p\u003e\n\u003cp\u003eLiu et al. (2022) conducted a systemic review on the prevention and management of falls and fear of falling (FoF) in PD patients and proposed a novel fall prevention theory. According to their model, physical exercise is considered one of the strong factors for improving patients’ fitness, reducing falls, and reducing their fear of falling, as recommended for primary prevention. In the case of mild PD symptoms, the combination of medication and low-intensity exercise, such as tai chi or walking, is recommended. This theory also emphasizes the active involvement of the entire community.\u003c/p\u003e\n\u003cp\u003eChen et al. (2022) discussed the Braak and dual-hit theories to provide theoretical principles to better understand the environmental triggers of PD. This theory emphasizes that olfactory structures and the gut are mucosal interfaces of humans exposed to environmental exposures, e.g., heavy metals such as manganese, and organic solvents such as trichloroethylene in the PD context. These environmental risk factors damage dopamine-containing neurons in the brain, causing neurodegeneration in PD-risk individuals (Chen et al., 2022).\u003c/p\u003e\n\u003cp\u003eMunoz-Pinto et al. (2021) proposed a unifying theory that connects various aspects of neuromicrobiology to explain PD etiology. The authors emphasized that microbial infections and imbalances in the gut microbiome are potential factors in PD onset and progression, thus targeting the gut‒brain axis. This theory explains that microbial factors cause neuroinflammation and neurodegeneration and, in turn, lead to PD symptoms.\u003c/p\u003e\n\u003cp\u003eYing and Vasanthi (2022) conducted a cross-sectional survey in the Malaysian context by using a self-administered and validated questionnaire to investigate exercise beliefs among PD patients during disease progression. This study used the health belief model (HBM) to evaluate the relationship between beliefs and preventive health behavior practices such as exercise. This study emphasized that despite strong supporting evidence of exercise benefits for PD patients, the National Parkinson Foundation reported that more than half of people (53%) with Parkinson’s disease do not exercise regularly. This lack of exercise seems to worsen their quality of life, making them physically weaker, leading to disease progression, and placing a burden on their caregivers.\u003c/p\u003e\n\u003ch2 id=\"_Toc184156996\"\u003eGaps in Theories\u003c/h2\u003e\n\u003cp\u003eMany theories have been proposed or used to understand the biological and neurological aspects of Parkinson’s disease (PD). Ahern et al. (2024), Liu et al. (2022), and Ying and Vasanthi (2022) emphasized the role of physical activity, such as exercise, in reducing PD progression. The theories of Chen et al. (2022) and Munoz-Pinto et al. (2021) highlight the effects of environmental triggers and microbial infections on the gut microbiome, respectively, to evaluate the risk of potential factors associated with PD onset and progression. Maggi et al. (2024) focused on affected PD patients with mild cognitive symptoms. Basas and Gozum (2023), Chen et al. (2023), and Lee et al. (2024), in their respective theoretical platforms, discuss more behavioral interventions, self-care, and management, patient families, and caregiver burdens related to PD patients. Despite these significant theories and findings, gaps related to the combination of risk factors, the factors that are protective and modifiable, the identification of at-risk PD patients, and preventive measures/interventions involving nonclinical components still exist. Most of the existing theories discuss the role of exercise; however, other factors, such as diet, geographical location, health care provider education, and caregiver education, are missing from a single model.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch3\u003eIntroduction\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eThe prevalence of\u0026nbsp;\u003c/strong\u003eParkinson’s disease (PD) has increased worldwide and is projected to reach approximately 13 million by 2040 (De Miranda et al., 2022). While the exact cause of PD is unknown, only 15–25% of cases are hereditary (NINDS, 2023), and the remaining 75–85% of cases are thought to be linked to exogenous factors such as environmental toxins, head trauma, and lifestyle factors (De Miranda et al., 2022). Hence, there is a critical need to explore and implement nonclinical preventive measures to reduce the risk of Parkinson's disease. By addressing lifestyle factors and environmental exposures and promoting early intervention, we can significantly mitigate the burden of PD on individuals and global healthcare systems.\u003c/p\u003e\n\u003cp\u003eTo the best of the literature review and knowledge of the author, no academic studies have attempted to include most of the exogenous factors in one model involving both PD patients and non-PD patients (healthy volunteers). Chen et al. (2023) used SEM theory to identify the barriers and facilitators influencing palliative care for PD patients and their caregivers living in China. However, this study was conducted in a terminal care environment. Few studies have focused on a combination of physical activity and sleep among PD patients.\u003c/p\u003e\n\u003cp\u003eThis research attempts to identify the key lifestyle, demographic, and environmental factors and their combinations with the greatest effects on the risk of developing Parkinson’s disease. This insight will allow one to identify high-risk populations and focus on developing public health strategies to mitigate the risk of developing Parkinson’s disease. To explore the exogenous factors, My Research question focuses on the “Do environmental risk factors differ significantly between PD and non-PD individuals?” which would help me to structure hypotheses about whether the combination of key environmental factors impacts the risk of developing PD. These hypotheses were analyzed as a part of the final model, and they were accepted/rejected on the basis of their p value.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eH1: Demographic and\u0026nbsp;\u003c/em\u003e\u003cem\u003esocioeconomic\u003c/em\u003e\u003cem\u003e\u0026nbsp;factors differ significantly between PD\u0026nbsp;\u003c/em\u003e\u003cem\u003epatients\u0026nbsp;\u003c/em\u003e\u003cem\u003eand non-PD\u0026nbsp;\u003c/em\u003e\u003cem\u003epatients\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eH2: Lifestyle and\u0026nbsp;\u003c/em\u003e\u003cem\u003ebehavioral factors\u003c/em\u003e\u003cem\u003e\u0026nbsp;differ significantly between PD\u0026nbsp;\u003c/em\u003e\u003cem\u003epatients\u0026nbsp;\u003c/em\u003e\u003cem\u003eand non-PD\u0026nbsp;\u003c/em\u003e\u003cem\u003epatients.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eH3: Health-related and\u0026nbsp;\u003c/em\u003e\u003cem\u003emedical history factors\u003c/em\u003e\u003cem\u003e\u0026nbsp;significantly\u0026nbsp;\u003c/em\u003e\u003cem\u003ediffer\u0026nbsp;\u003c/em\u003e\u003cem\u003ebetween PD\u0026nbsp;\u003c/em\u003e\u003cem\u003epatients\u0026nbsp;\u003c/em\u003e\u003cem\u003eand non-PD\u0026nbsp;\u003c/em\u003e\u003cem\u003epatients\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eH4: Environmental and\u0026nbsp;\u003c/em\u003e\u003cem\u003eoccupational exposure factors\u003c/em\u003e\u003cem\u003e\u0026nbsp;differ significantly between PD and non-PD individuals\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo investigate the differences between Parkinson's disease (PD) and non-PD individuals, we hypothesize that significant variations exist in various factors, including demographics, socioeconomic status, lifestyle behaviors, health history, and environmental exposures. In our analysis, we categorized all the predictors into four key groups for clarity and ease of interpretation:\u003c/p\u003e\n\u003cp\u003eThe demographic and socioeconomic factors included age, sex, race and ethnicity, education level, employment status, and income level. \u003cstrong\u003eLifestyle and\u0026nbsp;\u003c/strong\u003ebehavioral factors, such as alcohol use, smoking status, caffeine consumption, physical activity (both vigorous and moderate), and sleep duration, are considered here.\u0026nbsp;\u003cstrong\u003eThe health\u003c/strong\u003e\u003cstrong\u003e-related and\u0026nbsp;\u003c/strong\u003emedical history factor categories focus\u0026nbsp;on head injury history, anti-inflammatory medication use, and calcium channel blocker (CCB)\u0026nbsp;history. \u003cstrong\u003eEnvironmental and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eoccupational exposure factors,\u003c/strong\u003e including the residential environment (urban or rural), proximity to farms, access to private well water, occupation (e.g., military), toxicant exposure (such as glue and adhesives), and pesticide exposure (from jobs or sprays), are examined. By grouping the predictors in this way, we aim to provide a structured approach to understanding how these factors may differ between PD and non-PD individuals.\u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cp\u003eThis analysis utilized Fox Insight, an online clinical study building a large, diverse cohort of people with Parkinson\u0026rsquo;s disease and age-matched control volunteers who share information about their lived experience, genetics, and variability in Parkinson\u0026rsquo;s disease via a one-time environmental exposure questionnaire that was deployed between October 2017 and March 2019 (Gottesman et al, 2024). The participants were all 18 years and older and provided informed consent via the Fox Insight website.\u003c/p\u003e \u003cp\u003eThe study protocol was approved by the New England IRB (IRB#: 120160179, Legacy IRB#: 14\u0026ndash;236, Sponsor Protocol Number: 1, Study Title: Fox Insight). (Smolensky et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki and applicable regulatory requirements. The analysis exclusively utilized de-identified data from the Fox Insight database, ensuring adherence to ethical standards for secondary data analysis.\u003c/p\u003e \u003cp\u003eThe dataset will be analyzed via descriptive statistics, i.e., frequencies, means, and standard deviations for demographic and awareness variables. This was followed by inferential statistical analyses via the chi-square test to explore relationships/associations between environmental factors, lifestyle behaviors, and demographic factors. Finally, multivariate analysis via logistic regression was used to assess the combined effects of multiple risk factors on the likelihood of developing PD. This study conducts an analysis via the statistical software Python for accuracy and efficiency. All the statistical test results were considered significant if the p value was less than 0.05.\u003c/p\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eThe main dataset named \u0026ldquo;About You\u0026rdquo; consists of demographic information of all U.S.-based participants. This dataset contains all 55,000 participants with their basic information (e.g., age, sex, disease status). The one-time Environmental Exposure Questionnaire has 12 datasets (Alcohol, Caffeine, Smoking and Tobacco, Head Injury and Concussion, Pesticides at Work, Pesticides in Nonwork Settings, Residential History, Physical Activity and Sleep, Calcium Channel Blocker Medication History, Anti-Inflammatory Medication History, Occupation, and Toxicant) that were answered voluntarily by some participants from 55,000. All 12 datasets were merged into the main dataset by using a unique participant ID, \u0026ldquo;fox_insight_id\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePredictor variables\u003c/strong\u003e \u003cp\u003eA total of 24 predictor variables were selected to analyze risk factors between PD and non-PD patients, after which a prediction model was built.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcome/target variable\u003c/b\u003e: The outcome variable of this analysis was a predictive model that accurately predicted true PD cases. This was measured by splitting the data at a 70:30 ratio, with 70% of the data training a model on PD vs non-PD and testing it on 30% of the data in predicting the true PD cases.\u003c/p\u003e \u003cp\u003eDetailed descriptions of the predictor and outcome variables are provided in \u003cb\u003eAppendix A\u003c/b\u003e, as are the variable values and descriptions.\u003c/p\u003e\n\u003ch3\u003eData Analytic Plan\u003c/h3\u003e\n\u003cp\u003eThe initial step of this research consisted of downloading data files from the Fox Insight database. The main dataset \u0026ldquo;About You\u0026rdquo; is the base dataset for my analysis, and 11 datasets include the one-time environmental exposure questionnaire. All 11 datasets were merged into the base dataset via the unique identifier \u0026ldquo;fox_insight_id\u0026rdquo;. After carefully removing duplicates, preferring not to say, unknown, and missing, the total number of cases in the dataset reached approximately 1198.\u003c/p\u003e \u003cp\u003eExploratory analysis was then performed on this final PD dataset to create a pie chart showing PD cases and non-PD cases. This is performed to observe the differences in the outcome variable. Numerical variables such as age and BMI are shown in pair plots to determine the distribution to check the assumptions (normality, linearity, homogeneity, and homoscedasticity). Categorical variables such as sex, race, and education level were plotted as bar charts to visualize the distributions of various subgroups. A few categorical variables were recoded to make the data more consistent by transforming it into a more meaningful and manageable format. For instance, the race variable is recoded as White vs Nonwhite, and details of all other recoded variables are shown in \u003cb\u003eAppendix A\u003c/b\u003e. Several variables in the dataset, including employment and education, were recoded to simplify analysis and improve interpretability. This process involved reducing the number of categories in variables with multiple levels, such as education, to facilitate meaningful statistical analysis. Categories were grouped to address issues with small sample sizes or uneven distributions, as seen with the race variable, where certain groups were underrepresented. Additionally, recoding was carried out to enhance clarity and relevance by aligning the variables with widely used categorizations in the literature and addressing the research questions effectively. All the recoded variable descriptions are shown in \u003cb\u003eAppendix A\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSeveral models were created and analyzed before the logistic regression model was selected as the best model to predict whether a participant is at risk of developing Parkinson\u0026rsquo;s disease (PD), which is influenced by the combination of predictor variables. Additionally, backward stepwise pseudo-R\u003csup\u003e2\u003c/sup\u003e was performed to filter down the 24 variables to include only selected variables in the model. Owing to an imbalance of the dataset between PD and non-PD cases, SMOTE analysis was performed to resample the minority group (non-PD), and a random forest classifier was used to train and test the balanced dataset to address the bias.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStatistical assumption testing was performed for continuous predictor variables (age and BMI) to check for normality, linearity, homogeneity, and homoscedasticity, and the variables were evaluated as part of hypothesis testing via nonparametric tests such as the Mann‒Whitney U test for medians/IQRs. Age and BMI were skewed (Fig. 1), which was noted in the development of the logistic regression model. The remaining predictor variables are categorical, and cross-tabulations are performed via chi-square tests and variance inflation factors (VIFs) to detect multicollinearity. Boxplots are used to check assumptions of homoscedasticity or normality for regression. Most of these variables were skewed, which was noted in the development of the logistic regression model.\u003c/p\u003e\n\u003ch3\u003eKey Tables\u003c/h3\u003e\n\u003cp\u003eTables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provide an overview of the hypotheses that were supported and not supported in this research on the basis of the significance of p values above and below 0.05.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eDemographic and socioeconomic factor information for PD\u003c/em\u003e vs \u003cem\u003enon-PD participants\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-PD (n\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;1021)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;1198)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge \u003cem\u003e(Median/IQR)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.4 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.8 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 (27.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e584 (57.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e632 (52.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129 (72.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437 (42.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e566 (47.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI \u003cem\u003e(Median/IQR)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.44 (8.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.66 (5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.82 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWhite\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172 (97.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e992 (97.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1164 (97.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNon-White\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHispanic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNot Hispanic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173 (97.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1000 (97.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1173 (97.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHigh Education\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129 (72.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e696 (68.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e825 (68.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModerate Education\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (23.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259 (25.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300 (25.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLow Education\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66 (6.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEmployed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98 (55.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326 (31.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e424 (35.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNot Employed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79 (44.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e695 (68.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e774 (64.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHigh Income\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (56.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e596 (58.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e696 (58.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMiddle Income\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55 (31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291 (28.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e346 (28.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLow Income\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (12.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (13.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156 (13.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironmental exposure factors (lifestyle and behavioral factors; environment and occupational exposure factors; health-related and medical history)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic, n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-PD (n\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;354)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151 (85.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e843 (82.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e994 (82.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (14.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178 (17.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204 (17.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (30.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337 (33.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e391 (32.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123 (69.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e684 (66.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e807 (67.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaffeine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e677 (66.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e795 (66.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344 (33.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403 (33.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHead Injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66 (37.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e374 (36.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e440 (36.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePossibly\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (11.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143 (14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (13.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90 (50.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504 (49.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e594 (49.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOccupation(military)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158 (15.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171 (14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (92.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e863 (84.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1027 (85.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePesticide Exposure (JOB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175 (17.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188 (15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (92.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e846 (82.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1010 (84.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eToxicants (glues/adhesives)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (23.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208 (20.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249 (20.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136 (76.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e813 (79.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e949 (79.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous Physical Activity (hr/wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;1 hour\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (41.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306 (29.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379 (31.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1\u0026ndash;4 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68 (38.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e462 (45.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e530 (44.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5\u0026ndash;10 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (14.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e184 (18.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210 (17.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;10 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79 (6.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Physical Activity (hr/wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;1 hour\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (8.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97 (8.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1\u0026ndash;4 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (56.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e538 (52.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638 (53.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5\u0026ndash;10 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46 (25.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285 (27.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e331 (27.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;10 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (11.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;5 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5\u0026ndash;6 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (15.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142 (13.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169 (14.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e6\u0026ndash;7 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66 (37.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e385 (37.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e451 (37.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e7\u0026ndash;8 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68 (38.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e415(40.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e483 (40.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;8 hours\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54 (4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eUrban\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170 (96.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e915 (89.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1085 (90.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRural\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106 (10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113 (9.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate Well Water*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (11.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143 (14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164 (13.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156 (88.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e878 (85.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1034 (86.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePesticide Spray*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e433 (42.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492 (41.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e588 (57.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e706 (58.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocated near Farm*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201(19.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147 (83.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e820 (80.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e967 (80.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-inflammatory medication Hx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66 (37.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321 (31.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e387 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111 (62.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e700 (68.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e811 (67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCB Hx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94 (53.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e499 (48.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e593 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (46.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522 (51.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e605 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Hx\u0026thinsp;=\u0026thinsp;history, CCB\u0026thinsp;=\u0026thinsp;calcium channel blockers * indicates whether the participants\u0026apos; residential environmental conditions.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eUnivariate and bivariate analyses revealed notable demographic differences between the PD and non-PD groups. Individuals in the PD group were older, with a median age of 65.8 years, whereas those in the non-PD group were 59.4 years, indicating a significant difference in age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The sex distribution also varied significantly, with a greater proportion of males in the PD group (57.20%) than in the non-PD group (27.12%), where females were predominant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;2\u003cstrong\u003e)\u003c/strong\u003e. BMI was significantly lower in the PD group, with a median of 25.66, than in the non-PD group, with a median of 27.44 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Employment status further highlighted disparities, as fewer individuals in the PD group were employed (31.93%) than in the non-PD group (55.37%), indicating another significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that age, sex, BMI, and employment status are key factors distinguishing the two groups.\u003c/p\u003e\n\u003cp\u003eSimilarly, univariate and bivariate analyses of key predictors revealed several significant differences between the PD and non-PD groups. Occupational history revealed that individuals in the PD group were more likely to have served in the military (15.48% vs. 7.34%, P\u0026thinsp;=\u0026thinsp;0.01) and had greater exposure to pesticides through their jobs (17.14% vs. 7.34%, P\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig. 3). Vigorous physical activity also differed significantly, with the PD group engaging in fewer hours per week, as a larger proportion reported less than 1 hour of vigorous activity weekly (29.97% vs. 41.24%, P\u0026thinsp;=\u0026thinsp;0.03). The residential environment revealed that more individuals in the PD group than in the non-PD group lived in rural areas (10.38% vs. 3.95%, P\u0026thinsp;=\u0026thinsp;0.01). Additionally, exposure to pesticide spray was greater in the PD group, with 42.41% reporting such exposure compared with 33.33% in the non-PD group (P\u0026thinsp;=\u0026thinsp;0.03). These findings highlight occupational, environmental, and activity-related factors as key areas of difference between the two groups.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eKey Tables\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides the final logistic regression model. Of the 24 variables analyzed through backward adjusted-pseudo-R2 methodology for optimizing the model, 9 significant variables were selected as having the largest contribution to the model\u0026rsquo;s accuracy.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eFinal logistic regression model\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003cp\u003e(OR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower CI (OR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper CI (OR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation group (Higher)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePesticide exposure at Job (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous Physical Activity (1\u0026ndash;4 hrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban (small town)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium Channel Blockers Hx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe logistic regression model examined factors influencing the likelihood of developing Parkinson\u0026rsquo;s disease, utilizing data from 1,198 participants and 21 predictors. The model achieved a pseudo R\u003csup\u003e2\u003c/sup\u003e of 0.2014, indicating a moderate ability to explain the variance in diagnosis likelihood. Older age emerged as the most significant risk factor for developing Parkinson\u0026rsquo;s disease, with individuals experiencing nearly 39-fold higher odds as they aged (OR\u0026thinsp;=\u0026thinsp;39.37, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Conversely, a higher BMI was strongly protective, reducing the risk by 85% (OR\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Compared with women, men were found to have a markedly increased risk, with odds 3.5 times greater (OR\u0026thinsp;=\u0026thinsp;3.49, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Moreover, occupational exposure to pesticides doubled the likelihood of developing PD (2.2-fold increase, OR\u0026thinsp;=\u0026thinsp;2.23, p\u0026thinsp;=\u0026thinsp;0.01). Interestingly, regular alcohol consumption was associated with a 42% reduction in risk (OR\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;=\u0026thinsp;0.03), suggesting a potential protective effect. Surprisingly, engaging in 1\u0026ndash;4 hours of vigorous physical activity weekly was linked to a 75% higher risk of PD (OR\u0026thinsp;=\u0026thinsp;1.75, p\u0026thinsp;=\u0026thinsp;0.010), suggesting complex interactions between activity and prodromal symptoms. Compared with living in rural areas, living in a small town was associated with a 36% lower risk of PD (OR\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;=\u0026thinsp;0.04), possibly reflecting protective environmental or lifestyle factors. Although higher educational attainment was associated with a 30% reduction in risk (OR\u0026thinsp;=\u0026thinsp;0.70), the result was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.08), highlighting a potential trend that requires further study. Together, these findings provide a nuanced understanding of how demographic, lifestyle and environmental factors shape the risk of developing Parkinson\u0026rsquo;s disease.\u003c/p\u003e\n \u003cp\u003eThe total dataset was split into 70% and 30% to train and test the data for predictive modeling. After applying the synthetic minority oversampling technique (SMOTE) to balance the class distribution between PD and non-PD patients, the performance of both the logistic regression model and the random forest classifier model was evaluated for predicting Parkinson\u0026rsquo;s disease (PD) patients (class 1) and non-PD patients (class 0). Before SMOTE, logistic regression achieved high accuracy (88%) but struggled with predicting non-PD cases (class 0), with a low recall of 23%. However, it perfectly identified all PD cases (Class 1) with 100% recall. After SMOTE, the recall for non-PD cases (class 0) improved to 56%, but the precision decreased significantly to 0.29, and the overall accuracy decreased to 71%. In contrast, the random forest classifier achieved 81% accuracy after SMOTE, with the recall for non-PD cases (class 0) improving to 43% and the recall for PD cases (class 1) remaining high at 88%. While it performed well in predicting PD cases, its precision for non-PD cases (0.38) indicated challenges in correctly identifying non-PD individuals. Both models benefitted from SMOTE by improving the recall for non-PD cases, but logistic regression had a higher recall for Class 0, whereas the random forest classifier showed better overall accuracy and PD case (Class 1) prediction (Fig. 4). These results highlight the trade-off between improving class balance and maintaining strong performance in the majority class, with each model demonstrating different strengths post-SMOTE. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes various classification reports before and after SMOTE.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003ePerformance Metrics of the Classification Models Before and After the SMOTE\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogistic\u003c/p\u003e\n \u003cp\u003e(Before SMOTE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogistic\u003c/p\u003e\n \u003cp\u003e(After SMOTE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRandomForestClassifier (After SMOTE)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecision (Class 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecision (Class 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecall (Class 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecall (Class 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF1-Score (Class 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF1-Score (Class 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro Avg Precision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro Avg Recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMacro Avg F1-Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted Avg Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Results\u003c/h2\u003e \u003cp\u003eThe primary objective of this study was to explore the relationships between environmental risk factors and Parkinson's disease (PD) by examining demographic, socioeconomic, lifestyle, behavioral, health-related, and environmental factors in individuals with PD and those without PD. These findings confirm that environmental risk factors, including lifestyle choices, environmental exposures, and certain health-related factors, significantly differ between PD and non-PD individuals.\u003c/p\u003e \u003cp\u003eSpecifically, demographic factors such as older age and male sex were associated with a greater risk of developing PD. Socioeconomic factors such as unemployment or retirement were more common in the PD group, highlighting potential vulnerabilities tied to socioeconomic status. Behavioral factors such as lower levels of vigorous physical activity were also more prevalent among non-PD individuals, whereas higher activity levels in the PD group were associated with an increased risk of developing PD. This unexpected relationship may reflect reverse causality, where changes in behavior occur as a response to the disease rather than a contributing cause. These patterns align with findings from prior research that point to the complex interplay between age, sex, lifestyle, and environmental exposure in influencing PD risk.\u003c/p\u003e \u003cp\u003eIn line with our hypotheses, the results support the idea that lifestyle and behavioral factors, as well as occupational exposures (e.g., pesticide exposure), significantly influence the risk of developing PD. These results align with literature that points to environmental toxins (De Miranda et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), sex, and education (Al-Hakeem et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Boina, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as potential risk factors for PD, validating previous research that suggests a multifactorial etiology for this disease.\u003c/p\u003e \u003cp\u003eIn contrast to the findings of Chen et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who suggested that high physical activity is associated with a lower risk of developing Parkinson's disease (PD), this study revealed that increased vigorous physical activity was linked to a greater risk of developing PD. Specifically, individuals engaging in 1\u0026ndash;4 hours of vigorous activity per week, compared with those with less than 1 hour of activity, presented a significantly greater risk of developing PD. This finding was consistent across both bivariate analysis and logistic regression models, where higher levels of physical activity were associated with an increased risk of developing PD. These findings suggest that contrary to the protective effects observed in other studies, greater physical activity might not universally reduce PD risk and could be influenced by other contributing factors or confounding variables specific to our sample. Additionally, reverse causality may be a consideration\u0026mdash;individuals who have already been diagnosed with PD may have modified their activity levels owing to their health condition, which could result in the observed higher levels of physical activity in the PD group. In this case, the outcome (PD diagnosis) could have influenced lifestyle choices rather than the influence of physical activity on the development of PD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOne key limitation of this study is the reliance on self-reported data, which can introduce recall or social desirability biases. Additionally, the use of the mode to capture responses at different life stages (e.g., lifestyle, residence) might oversimplify the data. The mode represents the most frequent response, but it does not account for variations or changes over time. This may lead to a less nuanced understanding of how environmental and lifestyle factors, which can change over time, influence the development of Parkinson\u0026rsquo;s disease, potentially missing some important shifts or trends in participants' risk profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eFuture research could expand on the predictive modeling employed in this study by incorporating more granular data on genetic factors, which may help better understand the complex interaction between genetic predisposition and environmental influences on PD. Additionally, longitudinal studies could be implemented to examine how exposure to certain environmental factors over time directly influences the onset of PD, providing clearer evidence for causal relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImplications and Importance\u003c/h2\u003e \u003cp\u003eThis research underscores the critical role of environmental, lifestyle, and occupational factors in the development of Parkinson\u0026rsquo;s disease. Understanding these factors can inform nonclinical preventive measures, which are vital for reducing the incidence and impact of PD, especially as its prevalence continues to rise globally. The findings advocate for public health interventions that target lifestyle modifications and improve occupational safety to mitigate the environmental risks associated with PD. By addressing these modifiable risk factors early, we could reduce the burden of PD on individuals and healthcare systems worldwide. Therefore, this study contributes valuable insights for public health strategies, occupational safety policies, and future research into PD prevention.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI'm the sole author of this paper\u003c/p\u003e\u003ch1\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003eI would like to extend my heartfelt gratitude to my advisors, Dr. Katy Valentine \u0026amp; Dr. Stanley Nwoji, for their invaluable guidance and support throughout the completion of this thesis. Their expertise and encouragement have been instrumental in this academic journey.\u003c/p\u003e\n\u003cp\u003eAdditionally, I acknowledge the use of data from the Fox Insight Study. The Fox Insight Study (FI) is funded by The Michael J. Fox Foundation for Parkinson\u0026rsquo;s Research. I am deeply grateful to Parkinson\u0026rsquo;s community for their participation in this study, which made this research possible. The data used in the preparation of this study were obtained from the Fox Insight database (https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp) on 09/11/2024. For up-to-date information on the study, visit https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp. I declare that I have no conflicts of interest related to this research.\u003c/p\u003e\n\u003cp\u003eI am grateful to my peers at Harrisburg University for their encouragement and camaraderie during this process. I am also thankful to the HU library staff for their constructive feedback and support.\u003c/p\u003e\n\u003cp\u003eFinally, I would like to thank my family and friends for their unwavering support and patience, which has been a constant source of strength.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData used in the preparation of this thesis were obtained from the Fox Insight database (https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp). The analysis, derived datasets, and associated code developed specifically for this study are available from the corresponding author upon reasonable request, subject to compliance with data-sharing agreements and ethical guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLee, J., Chung, M., Kim, E., \u0026amp; Yoo, J.‐H. (2024). 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Theory of mind in mild cognitive impairment and Parkinson\u0026apos;s disease: The role of memory impairment. \u003cem\u003eCognitive, affective \u0026amp; behavioral neuroscience, 24\u003c/em\u003e(1), 156\u0026ndash;170. doi: 10.3758/s13415-023-01142-z\u003c/li\u003e\n \u003cli\u003eM\u0026uuml;ller-Nedebock, A. C., Dekker, M. C., Farrer, M. J., Hattori, N., Lim, S.-Y., Mellick, G. D., . . . Bardien, S. (2023). Different pieces of the same puzzle: a multifaceted perspective on the complex biological basis of Parkinson\u0026rsquo;s disease. \u003cem\u003enpj Parkinson\u0026apos;s Disease, 9\u003c/em\u003e, 110. doi:https://doi.org/10.1038/s41531-023-00535-8\u003c/li\u003e\n \u003cli\u003eMunoz-Pinto, M. F., Empadinhas, N., \u0026amp; Cardoso, S. M. (2021). The neuromicrobiology of Parkinson\u0026rsquo;s disease: a unifying theory. \u003cem\u003eAging Research Reviews, 70\u003c/em\u003e, 101396. doi:https://doi.org/10.1016/j.arr.2021.101396\u003c/li\u003e\n \u003cli\u003eNational Health Services. (2022, November 3). \u003cem\u003eParkinson\u0026apos;s disease.\u003c/em\u003e Retrieved from NHS: https://www.nhs.uk/conditions/parkinsons-disease/treatment/#:~:text=There\u0026apos;s%20currently%20no%20cure%20for, supportive%20therapies%2C%20 such%20as%20physiotherapy\u003c/li\u003e\n \u003cli\u003eNational Institute on Aging. (2022, April 14). Parkinson\u0026rsquo;s Disease: Causes, Symptoms, and Treatments. Retrieved from National Institute on Aging: https://www.nia.nih.gov/health/parkinsons-disease/parkinsons-disease-causes-symptoms-and-treatments\u003c/li\u003e\n \u003cli\u003eNewhouse, J. P. (2021). An ounce of prevention. Journal of Economic Perspectives. \u003cem\u003e35\u003c/em\u003e(2), 101-118. Retrieved from https://www.jstor.org/stable/27008031?seq=3\u003c/li\u003e\n \u003cli\u003eNINDS. (2023, January 30). \u003cem\u003eParkinson\u0026apos;s Disease: Challenges, Progress, and Promise.\u003c/em\u003e Retrieved from National Institute of Neurological Disorders and Stroke(NINDS): https://www.ninds.nih.gov/current-research/focus-disorders/parkinsons-disease-research/parkinsons-disease-challenges-progress-and-promise#toc-conclusion\u003c/li\u003e\n \u003cli\u003eOrganization, W. H. (2023). \u003cem\u003e2023 emerging technologies and scientific innovations: a global public health perspective.\u003c/em\u003e Retrieved from https://iris.who.int/bitstream/handle/10665/367116/WHO-SCI-RFH-2023.05-eng.pdf?sequence=1\u003c/li\u003e\n \u003cli\u003eOu, Z., Pan, J., Tang , S., Duan , D., Yu, D., Nong , H., \u0026amp; Wang, Z. (2021). Global Trends in the Incidence, Prevalence, and Years Lived With Disability of Parkinson\u0026apos;s Disease in 204 Countries/Territories From 1990 to 2019. \u003cem\u003eFront. Public Health, 9\u003c/em\u003e, 776847. doi:doi: 10.3389/fpubh.2021.776847\u003c/li\u003e\n \u003cli\u003ePoulia, K. A. (2024). Nourishing Neurons: Exploring the Role of Plant-Based Diets in Parkinson\u0026rsquo;s Disease Prevention. \u003cem\u003eKompass Nutrition \u0026amp; Dietetics, 4\u003c/em\u003e(1), 23\u0026ndash;24. Retrieved from https://doi.org/10.1159/000538286\u003c/li\u003e\n \u003cli\u003eQasim, M. M., Daghriri, A. A., Alanazi, O. A., AlOmari, L. I., Alzibali, K. F., Alrezqi, W. A., . . . Refai, H. M. (2023). Evaluation of Knowledge and Attitudes of the Population of Tabuk City Regarding Parkinson\u0026apos;s Disease: A Cross-Sectional Study. \u003cem\u003eCureus, 15\u003c/em\u003e(10), e46442. doi:https://doi.org/10.7759/cureus.46442\u003c/li\u003e\n \u003cli\u003eRajan, S., \u0026amp; Kaas, B. (2022). Parkinson\u0026apos;s Disease: Risk Factor Modification and Prevention. \u003cem\u003eSeminars in Neurology, 42\u003c/em\u003e(05), 626-638. doi:10.1055/s-0042-1758780\u003c/li\u003e\n \u003cli\u003eSarb, O. F., Sarb, A. D., Iacobescu, M., Vlad, I. M., Milaciu, M. V., Ciurmarnean, L., . . . Tantau, A. I. (2024). From Gut to Brain: Uncovering Potential Serum Biomarkers Connecting Inflammatory Bowel Diseases to Neurodegenerative Diseases. \u003cem\u003eInternational Journal of Molecular Sciences , 25\u003c/em\u003e(11), 5676. doi:https://doi.org/10.3390/ijms25115676\u003c/li\u003e\n \u003cli\u003eSchiess, N., Cataldi, R., Okun , M. S., \u0026amp; et al. (2022). Six Action Steps to Address Global Disparities in Parkinson Disease: A World Health Organization Priority. \u003cem\u003eJAMA Neurol., 79\u003c/em\u003e(9), 929-936. doi:10.1001/jamaneurol.2022.1783\u003c/li\u003e\n \u003cli\u003eScorza, F. A., Almeida, A. G., Scorza, C. A., \u0026amp; Finsterer, J. (2021). Prevention of Parkinson\u0026rsquo;s disease-related sudden death. \u003cem\u003eClinics, 76\u003c/em\u003e, e3266. doi:https://doi.org/10.6061/clinics/2021/e3266\u003c/li\u003e\n \u003cli\u003eSilva, A., Oliveria, R., Diogenes, G., Aguiar, M., Sallem, C., Lima, M., . . . Mendon\u0026ccedil;a, L. (2023). Premotor, nonmotor and motor symptoms of Parkinson\u0026apos;s Disease: A new clinical state of the art. \u003cem\u003eAging Research Reviews, 84\u003c/em\u003e, 101834. doi:https://doi.org/10.1016/j.arr.2022.101834\u003c/li\u003e\n \u003cli\u003eSmolensky, L., Amondikar, N., Crawford, K., Neu, S., Kopil, C. M., Daeschler, M., \u0026amp; Riley, L. (2020). Fox Insight collects online, longitudinal patient-reported outcomes and genetic data on Parkinson\u0026rsquo;s disease. \u003cem\u003eScientific Data, 7\u003c/em\u003e(67), 8228. doi:https://doi.org/10.1038/s41597-020-0401-2\u003c/li\u003e\n \u003cli\u003eThe Lancet Psychiatry. (2022, August). Prevention is better than cure. \u003cem\u003ePrevention is better than cure, 9\u003c/em\u003e(8), p. 601. doi:https://doi.org/10.1016/S2215-0366(22)00238-3\u003c/li\u003e\n \u003cli\u003eThrope, K. E. (2022). Racial trends in clinical preventive services use, chronic disease prevalence, and lack of insurance before and after the Affordable Care Act. \u003cem\u003eThe American journal of managed care, 28\u003c/em\u003e(4), e126\u0026ndash;e131. doi:https://doi.org/10.37765/ajmc.2022.88865\u003c/li\u003e\n \u003cli\u003eVellata, C., Belli , S., Balsamo, F., Giordano, A., Colombo, R., \u0026amp; Maggioni , G. (2021). Effectiveness of Telerehabilitation on Motor Impairments, Nonmotor Symptoms and Compliance in Patients With Parkinson\u0026apos;s Disease: A Systematic Review. \u003cem\u003eFrontiers in neurology, 12\u003c/em\u003e, 627999. doi: https://doi.org/10.3389/fneur.2021.627999\u003c/li\u003e\n \u003cli\u003eWilliams, D. (2024). Why so slow? Models of parkinsonian bradykinesia. \u003cem\u003eNature Reviews Neurosience, 25\u003c/em\u003e, 573-586. doi:https://doi.org/10.1038/s41583-024-00830-0\u003c/li\u003e\n \u003cli\u003eYing, A. C., \u0026amp; Vasanthi, R. K. (2022). A survey of exercise beliefs among people with Parkinson\u0026rsquo;s disease. \u003cem\u003ePhysiotherapy Quarterly, 30\u003c/em\u003e(3), 57\u0026ndash;63. doi:https://doi.org/10.5114/pq.2021.103556\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s Disease, Predictive Modeling, Lifestyle Factors, Environmental Exposures, Nonclinical Prevention and Public Health, Risk Factors","lastPublishedDoi":"10.21203/rs.3.rs-5679320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5679320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson’s disease (PD) is a neurodegenerative disorder with a rising global prevalence. While 15–25% of cases are hereditary, the rest are attributed to exogenous factors, such as environmental exposures and lifestyle choices. This study explores the relationships between various environmental, lifestyle, and health-related factors and PD risk via data from the Fox Insight database and analyzes descriptive statistics, logistic regression, and predictive modeling techniques. Key findings show that older age, male sex, lower BMI, unemployment (including both retired and unemployed individuals), and occupational pesticide exposure increase the risk of PD. Interestingly, higher BMI was associated with a reduced risk of PD, suggesting a potential protective effect, althoughthis may be influenced by reverse causality. Additionally, vigorous physical activity was found to be linked with an increased risk of PD, which could also reflect reverse causality, where individuals diagnosed with PD may increase their activity levels in response to their condition. These results highlight important modifiable factors for PD prevention and suggest areas for further research, particularly in understanding the complex interactions among lifestyle factors, environmental exposures, and disease onset.\u003c/p\u003e","manuscriptTitle":"Nonclinical Preventive Measures of Parkinson's Disease (PD): Identifying Key Lifestyle, Demographic, and Environmental Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-27 10:16:47","doi":"10.21203/rs.3.rs-5679320/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7599f30-f84d-4a3a-8b38-ab70e545078e","owner":[],"postedDate":"February 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44639131,"name":"Biological sciences/Neuroscience"},{"id":44639132,"name":"Earth and environmental sciences/Environmental sciences"},{"id":44639133,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":44639134,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-02-27T10:16:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-27 10:16:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5679320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5679320","identity":"rs-5679320","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}