Using Bivariate Latent Growth Model to Better Understand the Anxiety Symptom in Parkinson's Patients

Using Bivariate Latent Growth Model to Better Understand the Anxiety Symptom in Parkinson's Patients | 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 Research Article Using Bivariate Latent Growth Model to Better Understand the Anxiety Symptom in Parkinson's Patients qiushuang wang, Pugang Li, Yi Sun, YaoZhou Shi, Jing Bian, Hua-Shuo Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4925629/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 Objective This study utilizes the Bivariate Latent Growth Model to explore the developmental trajectories of trait anxiety and state anxiety, as well as the interrelationships between the trait anxiety and state anxiety. Methods We obtained six-year follow-up data from 475 Parkinson's disease patients through the Parkinson's Progression Markers Initiative. We employed latent growth models to explore the trajectories of anxiety, trait anxiety, and state anxiety. Subsequently, we used the Bivariate Latent Growth Model to investigate the longitudinal relationships between state anxiety and trait anxiety. Results The trajectories of anxiety, trait anxiety, and state anxiety were best described by a linear growth model. The intercept and slope of each were significantly correlated with the intercept, and the variance of both intercepts and the correlation between them were all significant (P < 0.05). Only the slopes of the total anxiety score and state anxiety were not significant, but the variance of their slopes was significant, indicating significant variability among individuals. The variance of the trait anxiety slope was also significant. The results of the Bivariate Latent Growth Model show significant associations among all intercept and slope factors (P < 0.018). Specifically, the intercept of trait anxiety is positively correlated with the intercept of state anxiety, and the slope of trait anxiety is positively correlated with the slope of state anxiety. The remaining path covariances between intercepts and slopes are negative. Conclusion Our research results indicate that among individuals with Parkinson's disease, those showing a higher growth trend in trait anxiety are more likely to experience a higher growth trend in state anxiety at a particular time point. Individuals scoring higher on trait anxiety are more likely to experience elevated levels of state anxiety at a specific time point. Individuals with higher initial levels may undergo smaller growth. For instance, individuals with higher levels of trait anxiety may exhibit lower growth in state anxiety or vice versa. It is evident that there is a close and reciprocal relationship between trait anxiety and state anxiety, with mutual influences. Parkinson's disease1 Bivariate Latent Growth Model2 Trait Anxiety3 State Anxiety4 Longitudinal study5 Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Parkinson's disease is the second-largest neurodegenerative disease globally, characterized by the primary pathological changes of degeneration and death of dopamine-producing neurons in the substantia nigra [ 1 , 2 ]. Clinically, the disease is predominantly characterized by motor symptoms such as tremors, bradykinesia, rigidity, and postural instability [ 3 ]. In addition to motor symptoms, Parkinson's disease is accompanied by various non-motor symptoms, including sleep disturbances, autonomic dysfunction, cognitive impairment, anxiety, depression, apathy, etc [ 4 , 5 ]. Some non-motor symptoms and signs have been shown to manifest several years to decades before clinical diagnosis of Parkinson's disease [ 6 ]. The annual incidence of Parkinson's disease is approximately (4 ~ 20) per 100,000 individuals [ 7 ]. It is estimated that by 2040, nearly 13 million people worldwide will be affected by Parkinson's disease [ 8 ]. The disease poses numerous challenges to patients, reducing their quality of life, gradually diminishing their daily living capabilities due to motor impairments, leading to various negative emotions, and increasing the burden on both family members and healthcare systems. People are familiar with Parkinson's disease (PD) primarily for its typical motor symptoms, but at least one-third of patients also suffer from anxiety disorders [ 9 ]. Anxiety is one of the earliest non-motor symptoms to appear in PD, emerging up to 20 years before the onset of motor symptoms [ 10 ]. It is characterized by symptoms such as lack of concentration, persistent worry, muscle tension, and an increase in tremor severity [ 11 , 12 ]. The presence of anxiety exacerbates the condition and is closely associated with the severity of motor symptoms, decreased quality of life, increased disability, and mortality rates, posing challenges to the treatment management of Parkinson's disease [ 13 , 14 ]. Research has found that even if individuals are not diagnosed with anxiety at the time of PD diagnosis, subsequent treatment or some symptoms of Parkinson's can induce anxiety [ 15 ]. Compared to those without anxiety, patients with both Parkinson's disease and anxiety exhibit greater disability and worse health conditions [ 16 ]. Anxiety can be classified into state anxiety and trait anxiety [ 17 ]. According to Spielberg's early formulation, anxiety is a unidimensional construct that includes both state and trait anxiety, seen as different sides of the same coin [ 18 ]. State anxiety is a transient response to events causing anxiety and changing situations from moment to moment. State anxiety is low when there is no danger or very little danger, while trait anxiety is a relatively stable tendency to react to experiences that induce anxiety [ 19 ]. Thus, trait anxiety is a relatively stable characteristic showing consistent individual differences in anxiety tendencies and can be considered a personality trait. There is a mutual influence between state anxiety and trait anxiety. Prolonged trait anxiety may increase the likelihood of experiencing state anxiety in specific situations, and vice versa. Different individuals may experience and express state anxiety and trait anxiety differently. Some individuals may be more prone to experiencing state anxiety through trait anxiety, while others may exhibit different reactions in various situations. Overall, the relationship between state anxiety and trait anxiety is complex and multi-layered. Understanding this relationship can contribute to a more comprehensive understanding of the nature of anxiety and provide more targeted approaches for intervention and treatment. Anxiety has received relatively less attention in the literature, with a predominant focus on the domain of depression. Research on anxiety primarily emphasizes its mechanisms, treatment, and correlations with other symptoms, such as anxiety's associations with depression [ 20 ], motor symptoms [ 21 ], autonomic nervous system functioning [ 22 , 23 ], and cognition [ 24 ]. The application of latent variable growth models (LGM) in the field of anxiety has been limited, mostly concentrating on studies involving adolescents [ 25 – 27 ]. To date, there has been no independent utilization of LGM to investigate the trajectory of anxiety in PD patients, possibly due to the relatively stable longitudinal nature of anxiety, where changes over several years may not be pronounced [ 28 , 29 ], resulting in less-than-ideal research outcomes. This study aims to explore the anxiety trajectory in Parkinson's disease patients, providing a deeper understanding of the dynamic evolution of anxiety within individuals. We categorize anxiety into trait anxiety and state anxiety, utilizing a bivariate latent growth model to investigate the developmental relationship between state anxiety and trait anxiety. Such research contributes to a more profound comprehension of the changing trajectory of anxiety, facilitating the development of more effective intervention measures to enhance individual mental well-being. 2 Subjects and methods 2.1 Data and sample We obtained data from the Parkinson's Progression Markers Initiative (PPMI),a publicly available database. In 2010, The Michael J. Fox Foundation and a core group of academic scientists and industry partners launched the The Parkinson's Progression Markers Initiative (PPMI) aims to investigate much-needed biomarkers for the onset and progression of Parkinson's disease. Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database( www.ppmi-info.org/access-data-specimens/download-data),RRID:SCR_00643 1. For up-to-date information on the study, visit www.ppmi-info.org.This study includes data from PD patients in the PPMI database from 2010 to 2024, which was visited at 12-month intervals. Data with missing basic demographic information and missing rates greater than 20% were excluded. The last six data counts were left, the highest rate of missing data was 14.7%, and the number of samples was 475 participants.The sample selection process is outlined in Fig. 1 . None of our participants received treatment at baseline, but underwent confirmative assessments, including clinical and cognitive evaluations, imaging examinations, and biological sampling, which were approved by the local participant Central Institutional Review Board. All participants provided written informed consent prior to enrollment. 2.2 Measures The State-Trait Anxiety Inventory (STAI) is a widely used psychological assessment tool for measuring anxiety levels [ 30 ]. It consists of two component scales: State Anxiety and Trait Anxiety. These two scales can be used independently to assess state and trait anxiety, respectively. The STAI questionnaire comprises 40 items, with each subscale containing 20 items. Participants are evaluated on their levels of state and trait anxiety based on their responses to these items. Responses are typically rated on a 4-point scale reflecting the frequency of experiences during a specific period: "Almost Never (1)," "Sometimes (2)," "Often (3)," and "Almost Always (4)". The total scores for trait and state anxiety range from a minimum of 20 to a maximum of 80, with higher scores indicating higher levels of anxiety in the respective domains. 2.3 Statistical analysis First, descriptive statistics were performed on the data, with continuous variables represented by mean ± standard deviation and categorical variables by frequency counts (%). Next, Pearson correlation analysis was used to explore the relationships between variables. To understand the trajectory of the variables, we made two assumptions based on previous research results: first, that the trajectory of the variable is a no-growth model, and second, that the trajectory of the variable is a linear growth model. The best model was then selected based on fit indices. The linear growth model includes two latent variables: the intercept factor and the slope factor. The LGM uses the mean and variance parameters of these latent variables to describe within-group and between-group differences. Specifically, the mean of the intercept factor represents the average initial state, while the variance of the intercept factor indicates the degree of individual differences at a specific time point. The greater the variance, the more significant the initial differences between individuals. The mean of the slope factor represents the average growth rate between time points, and the variance of the slope factor reflects the magnitude of individual differences in growth rates. The bidirectional arrow between these two factors indicates their correlation. To further explore the relationship between state anxiety and trait anxiety, this study employed the bivariate latent growth curve model, which combines two latent growth models, with the parameter interpretations being the same as mentioned above. This model is widely used to study the dynamic relationships between two variables over time, as well as their covariation, interactions, and individual differences. It is particularly useful for long-term studies aiming to understand the complex dynamic relationships between variables. This study used the bivariate latent growth curve model to examine the longitudinal patterns and correlations between state anxiety and trait anxiety. For the missing data, we use Maximum likelihood (ML) estimation to estimate the parameters of the model. To assess the model fit degree, we rely on Comparative Fit Index (CFI), Tucker-Lewis Index (TLI) and Standardized Root Mean Square Residual(SRMR) values, as the χ2 goodness-of-fit statistic can be overly sensitive for large sample sizes, and therefore, we do not employ it. For CFI and TLI,a Value at 0.8 ~ 0.9 represent generic model fits and are also acceptable, a value above 0.90 is considered acceptable, and a value exceeding 0.95 indicates a good fit. SRMR examines the fit of the model by the size of the residue, and its values range from 0 to 1 and indicate a good model fit when the value is less than 0.08. To implement the necessary model and conduct the analysis, we utilize Mplus 8.9. Descriptive analysis and plotting are performed using SPSS version 25.0 and R (version 4.2.3). The test level is set at a p-value of 0.05. 3 Results 3.1 Demographic information From Table 1 , it can be seen that there are 337 participants (70.9%) in our sample who are aged 56 and above, with 178 females (37.5%) and 297 males (62.5%). The majority of the sample is White, accounting for 443 participants (93.3%), and the education level is concentrated between 13–23 years (78.9%). The Hoehn and Yahr stages are mainly concentrated in stages 1 and 2, with a total of 463 participants (97.5%). The average age at onset is 59.02 ± 9.95 years old, and the average disease duration is 1.19 ± 1.54 years. Table 1 Characteristics of study population Variables Classification Statistics(N = 475) Age 65 years old 194(40.8%) Gender female 178(37.5%) male 297(62.5%) Years of Education 23years 8(1.7%) Race white 443(93.3%) No white 32(6.7%) Hoehn and Yahr stage0 2(0.4%) stage1 192(40.4%) stage2 271(57.1%) stage3-5 10(2.1%) Family history yes 170(35.8%) no 305(64.2%) Age at PD Symptom Onset 59.02 ± 9.95 Duration from PD Diagnosis 1.19 ± 1.54 Figure 2 illustrates that, with the progression of the disease, there is little change in the total anxiety score as well as the scores for state anxiety and trait anxiety. This further validates previous research findings [ 28 , 29 ]. Additionally, Fig. 3 , through Pearson correlation analysis, describes the relationship between trait anxiety and state anxiety in PD patients, showing a general positive correlation between the two. Despite observing minimal changes in the scores of trait anxiety and state anxiety through statistical description, it is essential to explore the correlation between these two aspects. Note Numbers in the graph represent correlation coefficients. *p < 0 .05; **p < 0 .01;***p < 0 .001. 3.2 Univariate Latent Growth Curve Model Based on the results of statistical descriptions, we constructed both non-growth models and linear growth models for anxiety, trait anxiety, and state anxiety separately, as shown in Table 2 From the results, it can be observed that the fit indices of the linear growth models for all variables are superior to those of the non-growth models. Therefore, we chose the linear growth model to examine the patterns of change and individual differences in anxiety, trait anxiety, and state anxiety. We utilized univariate latent growth curve models to explore the developmental trajectories of each variable, as detailed in Table 3 The results from the analysis of univariate latent growth curve models align with the descriptive statistics depicted in Fig. 2 , affirming a strong fit of the LGM to the dataset. Table 2 Fit Indices of Latent Growth Models for Each Variable. Variables Model X2/df CFI TLI SRMR Anxiety No growth 73.149/19 0.951 0.961 0.066 Linear growth 17.278/16 0.999 0.999 0.032 Trait Anxiety No growth 103.537/19 0.932 0.946 0.064 Linear growth 20.052/16 0.997 0.997 0.031 State Anxiety No growth 39.749/19 0.975 0.980 0.055 Linear growth 17.032/16 0.999 0.999 0.036 Table 3 Parameter Estimates of Univariate Latent Growth Curve Model. Variables Intercept Slope Correlation mean variance mean variance Anxiety 66.215*** 257.927*** 0.122 5.311*** −7.286* State Anxiety 33.030*** 64.822*** −0.056 1.235*** −2.378* Trait Anxiety 33.176*** 74.931*** 0.177* 1.651*** −2.173* Note:*p < 0.05; **p < 0.01;***p < 0.001. Concerning total anxiety scores, the intercept mean is 66.215 (P < 0.001), accompanied by a variance of 257.927 (P < 0.001). This indicates an initial anxiety score level of 66.215, with noteworthy variability among individuals in their initial state. The slope mean is 0.122 (P = 0.434), insignificantly suggesting no overall linear increase or decrease in total anxiety scores. Nevertheless, the significant variance of the slope implies substantial individual variability, indicating potential significant differences in growth trends despite the mean being nonsignificant. The correlation between the intercept and slope of total anxiety scores is -7.286 (P = 0.045), signifying a negative correlation at the individual level. This might indicate that, in certain individuals, higher initial states could correspond to smaller growth. For state anxiety, the intercept mean is 33.030 (P < 0.001), coupled with a variance of 64.822 (P < 0.001). This suggests an initial state anxiety level of 33.030, with significant variability among individuals in their initial state. The slope mean is -0.056 (P = 0.528), nonsignificantly indicating no overall linear increase or decrease in state anxiety scores. Nonetheless, the significant variance of the slope implies considerable individual variability, suggesting potential significant differences in growth trends despite the mean being nonsignificant. The correlation between the intercept and slope of state anxiety scores is -2.378 (P = 0.048), revealing a negative correlation at the individual level. This may suggest that, in certain individuals, higher initial states could correspond to smaller growth.In the latent growth model for state anxiety, we found that the linear growth model fits better than the no-growth model, although the slope factor is not significant. We analyzed this situation and identified several possible reasons: low variability in the slope factor, meaning that individual changes might not be large enough to reach statistical significance; insufficient measurement time points, where the distribution or number of time points might not be adequate to capture significant linear changes. We considered the model's fit indices, theoretical foundation (anxiety symptoms in Parkinson's patients tend to worsen gradually), explanation, and prediction. Despite the non-significance of the slope factor, the linear growth model's fit indices are significantly better than those of the no-growth model, and it holds theoretical and practical significance. Therefore, choosing the linear growth model is reasonable. For trait anxiety, the intercept mean is 33.176 (P < 0.001), and the slope is 0.177 (P = 0.030). This indicates an initial trait anxiety level of 33.176, with a slight linear upward trend in trait anxiety scores over the six follow-up times. The annual increase in trait anxiety scores is approximately 0.117 points, indicating a worsening of patients' trait anxiety symptoms each year. Additionally, the variance of the intercept is 74.931 (P < 0.001), and the variance of the slope is 1.651 (P < 0.001), both of which are significant, indicating individual differences in the initial level and rate of increase of trait anxiety. The correlation between the intercept and slope of trait anxiety scores is -2.173 (P = 0.022), suggesting that individuals with higher initial states may experience smaller growth. 3.3 Bivariate latent growth curve model To delve deeper into the relationship between state anxiety and trait anxiety, we conducted a bivariate latent growth curve model analysis. The results indicated a good model fit, with a chi-square/degrees of freedom ratio of 796.496/56, CFI (Comparative Fit Index) of 0.859, TLI (Tucker-Lewis Index) of 0.834, and SRMR (Standardized Root Mean Square Residual) of 0.044.The interaction effects in the path analysis are presented in Table 4 , and for a more intuitive understanding of these interactions, refer to Fig. 4 . All P-values in the figure are greater than 0.05, allowing us to reject the hypothesis of a covariance of 0. This leads to the conclusion that all intercept and slope factors have significant associations. Specifically, the slope of trait anxiety is positively correlated with the slope of state anxiety, as well as the intercept of trait anxiety being positively correlated with the intercept of state anxiety. the growth trend in trait anxiety is positively correlated with the growth trend in state anxiety, indicating that individuals with a higher growth trend in trait anxiety are more likely to experience a higher growth trend in state anxiety at a certain time point. The positive correlation coefficient between the intercepts suggests a positive relationship at the individual level, indicating that individuals with higher scores in trait anxiety are more likely to experience higher levels of state anxiety at a certain time point. The remaining covariances between intercepts and slopes are negative, indicating a negative dynamic relationship between these two concepts. This suggests that in some individuals, those with higher initial states may experience smaller growth, for example, individuals with higher levels of trait anxiety may show lower growth in state anxiety. Table 4 Parameter estimates from the LGM analysis of the relationship between State Anxiety and Trait Anxiety. Factor covariance Estimate SE P value ITRA with ISTA 69.115 5.116 < 0.001 ITRA with SSTA -5.241 0.863 < 0.001 STRA with ISTA -3.054 0.775 < 0.001 STRA with SSTA 2.288 0.189 < 0.001 STRA with ITRA -2.147 0.833 0.010 SSTA with ISTA -2.253 0.956 0.018 Note: ISTA: Intercept of State Anxiety; SSTA: Slope of State Anxiety; ITRA: Intercept of Trait Anxiety; STRA: Slope of Trait Anxiety; SE:standard error. Note The red arrows represent positive covariances, while the blue arrows represent negative covariances. All arrows in the diagram are significant, and for the sake of simplification, standard deviations and p-values are not indicated. 4 Discussion This study employed a bivariate latent basic growth curve model to explore the relationship between changes in state anxiety and trait anxiety. Initially, a univariate latent growth curve model was utilized to examine the developmental trajectories between the two variables, revealing subtle changes. The longitudinal process of anxiety was observed to be relatively stable, as indicated by the heatmap, suggesting a correlation between the two and sparking further curiosity for exploration. Subsequently, a bivariate latent growth curve model was employed to investigate the relationship between state anxiety and trait anxiety. The analysis results indicated a significant correlation in the changes of trait anxiety and state anxiety over time among Parkinson's patients. At the individual level, those exhibiting a higher growth trend in trait anxiety were more likely to experience a higher growth trend in state anxiety at a specific time point. Individuals scoring higher in trait anxiety were also more likely to experience higher levels of state anxiety at a particular time point, and vice versa. Evidence suggests that individuals with high trait anxiety are prone to developing stress-induced depression or anxiety disorders due to heightened stress responsiveness, increased passive coping responses to environmental challenges, cognitive function changes, and decreased social competitiveness [ 31 ], aligning with the findings of our study. Negative covariances between intercepts and slopes indicate a negative dynamic relationship between these two concepts. This may suggest that in some individuals, those with higher initial states may experience smaller growth, such as individuals with higher levels of trait anxiety potentially showing lower growth in state anxiety, or vice versa. The intimate correlation and mutual influence between trait anxiety and state anxiety are evident. Conventional state anxiety is a normal response to sudden situations and stressful circumstances. However, prolonged and intense anxiety, if not alleviated, may lead to self-suppression, causing long-term damage to both psychological and physiological health [ 14 , 32 , 33 ]. Trait anxiety, being more stable, is often challenging to improve rapidly, influenced by factors such as individual physiology and genetics. State anxiety is typically short-term and associated with specific situations or events. Once the situation eases, state anxiety may alleviate. Therefore, focusing on improving state anxiety appears to be a more direct and effective approach. As state anxiety improves, it impacts the accumulation of trait anxiety, thereby reducing trait anxiety. The decrease in trait anxiety, in turn, contributes to the reduction of state anxiety, creating a cyclic process of continual reduction. Studies have shown that dysregulation of the locus coeruleus in the brain can lead to anxiety [ 34 , 35 ]. This is because the nerve endings of norepinephrine neurons are highly concentrated in the locus coeruleus, accounting for 70% of the extracellular norepinephrine in the brain. Norepinephrine is a major monoamine neurotransmitter responsible for arousal, alertness, concentration, and activating the stress response [ 36 ]. Therefore, when the locus coeruleus is dysregulated, it can lead to anxiety. Research has also indicated that the loss of serotonergic neurons can lead to anxiety symptoms [ 37 ]. Serotonin consists of 14 types of receptors, with serotonin playing a crucial role in the etiology of anxiety. Serotonin is a neurotransmitter that plays a vital role in regulating mood, fear, aggregation, and modulating anxiety [ 38 ]. The dysfunction of the hypothalamic-pituitary-adrenal axis [ 39 ] and the gamma-aminobutyric acid system [ 40 ] is also significantly related to anxiety. Research on the mechanisms of anxiety is gradually advancing, which is highly beneficial for the treatment of anxiety. Currently, the primary methods for treating anxiety include medication and non-pharmacological interventions. Medication options encompass the use of Benzodiazepines, tricyclic agents, and other antidepressants [ 41 ]. Non-pharmacological treatment approaches involve Cognitive Behavioral Therapy (CBT), Transcranial Magnetic Stimulation (TMS), and Deep Brain Stimulation (DBS) [ 42 – 44 ]. However, existing pharmacological treatments are insufficient to alleviate the complications of Parkinson's disease. Hence, alternative treatment options, such as yoga, aromatherapy, and music therapy, are being explored [ 45 – 47 ]. In comparison to specialized treatments targeting state anxiety, personally acquiring effective coping and management methods might be more accessible. For instance, learning effective stress management techniques, such as time management, problem-solving, and setting realistic goals, can contribute to reducing state anxiety. Employing relaxation techniques, such as deep breathing, progressive muscle relaxation, or meditation, when facing tense situations can help alleviate physical tension. Taking control of one's emotions, acquiring effective emotion regulation skills, seeking support, and maintaining a positive attitude can also contribute to reducing state anxiety. Additionally, seeking help from friends, family, or professional mental health experts when faced with challenging situations is a viable option. Evidence suggests that more severe symptoms of depression or anxiety can deteriorate social functioning and impact daily life activities [ 48 , 49 ]. Therefore, improving anxiety symptoms can also enhance the social functioning and daily living activities of Parkinson's disease patients. Anxiety symptoms are also associated with motor function [ 50 ], depression [ 20 ], sleep disorders [ 51 ], cognitive impairment [ 52 ], and autonomic dysfunction [ 23 ]. Addressing and treating these symptoms can also alleviate anxiety symptoms. This indicates that we can consider treating the anxiety symptoms of Parkinson's patients from this perspective. This study also has some limitations: First, due to the complex overlap of anxiety with depression, autonomic dysfunction, and PD-related motor symptoms [ 47 ], it is often challenging to identify and lacks well-designed studies and PD-specific tools. This highlights a direction for future research and development. Second, Self-reported measures of anxiety and potential biases in PD patients may limit the value of this study. Third, the predominantly Caucasian participant group may limit the generalizability of our study results. Fourth, anxiety is a relatively stable process, and although our fit indices (CFI, TLI) fall within an acceptable range, they are not very high. Thus, expanding the sample size, increasing the frequency of follow-ups, and conducting a more precise examination of the trajectory of anxiety are warranted. Additionally, considering other models from different perspectives could provide a more comprehensive understanding of anxiety in individuals with Parkinson's disease. 5 Conclusion Based on the analysis of 6-year longitudinal data from the PPMI, our study elucidates the developmental trajectories of state anxiety and trait anxiety. Furthermore, using a bivariate latent growth curve model, we reveal the interactions between state anxiety and trait anxiety. Our findings suggest that at the individual level, those with a higher growth trajectory in trait anxiety are more likely to experience a higher growth trajectory in state anxiety at a specific point in time. Individuals scoring higher on trait anxiety are also more likely to experience higher levels of state anxiety at a particular time point. Individuals with higher initial states may undergo smaller growth, indicating that those with higher levels of trait anxiety may exhibit lower growth in state anxiety, and vice versa. The intimate correlation and mutual influence between trait anxiety and state anxiety highlight the interconnectedness of these two constructs. Considering the interplay between them, interventions aimed at improving state anxiety in Parkinson's patients may contribute to reducing trait anxiety, enhancing overall mental health, and preventing them from becoming individuals prone to anxiety. Declarations Acknowledgements. Funding: PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's, AskBio, Avid Radiopharmaceuticals, BIAL, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics. Authors ’ contributions: QW contributed to the conception, design, data analysis, and interpretation of the study. She conducted literature review, data collection, and manuscript drafting. JB YS and YS were involved in data collection and preprocessing. PL and HZ played crucial roles in the design and conceptualization of the study, as well as providing essential guidance and supervision throughout the entire research process. Collectively, all authors reviewed and contributed to the manuscript's development, ensuring accuracy and scientific rigor. Funding This work was supported by the Social Development Project of Xuzhou City (Number: KC21267). Availability of data and materials Parkinson’s Progression Markers Initiative (PPMI) data is available at www.ppmi-info.org/access-data-specimens/download-data Conflict of interest/Competing interests The authors declare that they have no competing interests. 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Acta Neurol Scand 134(6):458–466 Mariani LL, Doulazmi M, Chaigneau V, Brefel-Courbon C, Carrière N, Danaila T, Defebvre L, Defer G, Dellapina E, Doé de Maindreville A et al (2019) Descriptive analysis of the French NS-Park registry: Towards a nation-wide Parkinson's disease cohort? Parkinsonism Relat Disord 64:226–234 Broen MP, Köhler S, Moonen AJ, Kuijf ML, Dujardin K, Marsh L, Richard IH, Starkstein SE, Martinez-Martin P, Leentjens AF (2016) Modeling anxiety in Parkinson's disease. Mov disorders: official J Mov Disorder Soc 31(3):310–316 Cui SS, Du JJ, Fu R, Lin YQ, Huang P, He YC, Gao C, Wang HL, Chen SD (2017) Prevalence and risk factors for depression and anxiety in Chinese patients with Parkinson disease. BMC Geriatr 17(1):270 Stanković I, Petrović I, Pekmezović T, Marković V, Stojković T, Dragašević-Mišković N, Svetel M, Kostić V (2019) Longitudinal assessment of autonomic dysfunction in early Parkinson's disease. Parkinsonism Relat Disord 66:74–79 Petkus AJ, Filoteo JV, Schiehser DM, Gomez ME, Hui JS, Jarrahi B, McEwen S, Jakowec MW, Petzinger GM (2020) Mild cognitive impairment, psychiatric symptoms, and executive functioning in patients with Parkinson's disease. Int J Geriatr Psychiatry 35(4):396–404 Hale WW 3rd, Klimstra TA, Wijsbroek SA, Raaijmakers QA, Muris P, van Hoof A, Meeus WH (2009) [Developmental trajectories of anxiety disorder symptoms in adolescents: a five-year prospective community study]. Tijdschrift voor psychiatrie 51(1):21–30 Shepard CA, Rufino KA, Lee J, Tran T, Paddock K, Wu C, Oldham JM, Mathew SJ, Patriquin MA (2023) Nighttime Sleep Quality and Daytime Sleepiness Predicts Suicide Risk in Adults Admitted to an Inpatient Psychiatric Hospital. Behav sleep Med 21(2):129–141 Tochigi M, Usami S, Matamura M, Kitagawa Y, Fukushima M, Yonehara H, Togo F, Nishida A, Sasaki T (2016) Annual longitudinal survey at up to five time points reveals reciprocal effects of bedtime delay and depression/anxiety in adolescents. Sleep Med 17:81–86 Abou Kassm S, Naja W, Haddad R, Pelissolo A (2021) The Relationship Between Anxiety Disorders and Parkinson's Disease: Clinical and Therapeutic Issues. Curr psychiatry Rep 23(4):20 de la Riva P, Smith K, Xie SX, Weintraub D (2014) Course of psychiatric symptoms and global cognition in early Parkinson disease. Neurology 83(12):1096–1103 Tluczek A, Henriques JB, Brown RL (2009) Support for the reliability and validity of a six-item state anxiety scale derived from the State-Trait Anxiety Inventory. J Nurs Meas 17(1):19–28 Weger M, Sandi C (2018) High anxiety trait: A vulnerable phenotype for stress-induced depression. Neurosci Biobehav Rev 87:27–37 Fan JY, Chang BL, Wu YR (2016) Relationships among Depression, Anxiety, Sleep, and Quality of Life in Patients with Parkinson's Disease in Taiwan. Parkinson's disease 2016:4040185 Fereshtehnejad SM, Shafieesabet M, Farhadi F, Hadizadeh H, Rahmani A, Naderi N, Khaefpanah D, Shahidi GA, Delbari A, Lökk J (2015) Heterogeneous Determinants of Quality of Life in Different Phenotypes of Parkinson's Disease. PLoS ONE 10(9):e0137081 McCall JG, Al-Hasani R, Siuda ER, Hong DY, Norris AJ, Ford CP, Bruchas MR (2015) CRH Engagement of the Locus Coeruleus Noradrenergic System Mediates Stress-Induced Anxiety. Neuron 87(3):605–620 Morris LS, McCall JG, Charney DS, Murrough JW (2020) The role of the locus coeruleus in the generation of pathological anxiety. Brain Neurosci Adv 4:2398212820930321 Ressler KJ, Nemeroff CB (2001) Role of norepinephrine in the pathophysiology of neuropsychiatric disorders. CNS Spectr 6(8):663–666 Schrag A, Politis M (2016) Serotonergic loss underlying apathy in Parkinson's disease. Brain 139(Pt 9):2338–2339 Lemonde S, Turecki G, Bakish D, Du L, Hrdina PD, Bown CD, Sequeira A, Kushwaha N, Morris SJ, Basak A et al (2003) Impaired repression at a 5-hydroxytryptamine 1A receptor gene polymorphism associated with major depression and suicide. J neuroscience: official J Soc Neurosci 23(25):8788–8799 Laufer S, Engel S, Knaevelsrud C, Schumacher S (2018) Cortisol and alpha-amylase assessment in psychotherapeutic intervention studies: A systematic review. Neurosci Biobehav Rev 95:235–262 Mann JJ, Oquendo MA, Watson KT, Boldrini M, Malone KM, Ellis SP, Sullivan G, Cooper TB, Xie S, Currier D (2014) Anxiety in major depression and cerebrospinal fluid free gamma-aminobutyric acid. Depress Anxiety 31(10):814–821 Khatri DK, Choudhary M, Sood A, Singh SB (2020) Anxiety: An ignored aspect of Parkinson's disease lacking attention. Biomed pharmacotherapy = Biomedecine pharmacotherapie 131:110776 Fernandez L, Major BP, Teo WP, Byrne LK, Enticott PG (2018) Assessing cerebellar brain inhibition (CBI) via transcranial magnetic stimulation (TMS): A systematic review. Neurosci Biobehav Rev 86:176–206 Malek N (2019) Deep Brain Stimulation in Parkinson's Disease. Neurol India 67(4):968–978 Zhang Q, Yang X, Song H, Jin Y (2020) Cognitive behavioral therapy for depression and anxiety of Parkinson's disease: A systematic review and meta-analysis. Complement Ther Clin Pract 39:101111 Gong M, Dong H, Tang Y, Huang W, Lu F (2020) Effects of aromatherapy on anxiety: A meta-analysis of randomized controlled trials. J Affect Disord 274:1028–1040 Kwok JYY, Kwan JCY, Auyeung M, Mok VCT, Lau CKY, Choi KC, Chan HYL (2019) Effects of Mindfulness Yoga vs Stretching and Resistance Training Exercises on Anxiety and Depression for People With Parkinson Disease: A Randomized Clinical Trial. JAMA Neurol 76(7):755–763 Rutten S, Ghielen I, Vriend C, Hoogendoorn AW, Berendse HW, Leentjens AF, van der Werf YD, Smit JH, van den Heuvel OA (2015) Anxiety in Parkinson's disease: Symptom dimensions and overlap with depression and autonomic failure. Parkinsonism Relat Disord 21(3):189–193 Chen YR, Tan CH, Su HC, Chien CY, Sung PS, Lin TY, Lee TL, Yu RL (2022) Investigating the interaction between neuropsychiatry features and daily activities on social function in patients with Parkinson's disease with mild cognitive impairment. BJPsych open 8(6):e205 Saris IMJ, Aghajani M, van der Werff SJA, van der Wee NJA, Penninx B (2017) Social functioning in patients with depressive and anxiety disorders. Acta psychiatrica Scandinavica 136(4):352–361 van der Velden RMJ, Broen MPG, Kuijf ML, Leentjens AFG (2018) Frequency of mood and anxiety fluctuations in Parkinson's disease patients with motor fluctuations: A systematic review. Mov disorders: official J Mov Disorder Soc 33(10):1521–1527 Palmeri R, Lo Buono V, Bonanno L, Sorbera C, Cimino V, Bramanti P, Di Lorenzo G, Marino S (2019) Potential predictors of quality of life in Parkinson's Disease: Sleep and mood disorders. J Clin neuroscience: official J Neurosurgical Soc Australasia 70:113–117 Jones JD, Mangal P, Lafo J, Okun MS, Bowers D (2016) Mood Differences Among Parkinson's Disease Patients With Mild Cognitive Impairment. J Neuropsychiatry Clin Neurosci 28(3):211–216 Additional Declarations No competing interests reported. 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. 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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-4925629","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341371331,"identity":"528173d9-b830-405f-95c1-ac0a8a25f02d","order_by":0,"name":"qiushuang wang","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"qiushuang","middleName":"","lastName":"wang","suffix":""},{"id":341371333,"identity":"f9c00e44-9885-4db9-9be3-e14fbdc111a1","order_by":1,"name":"Pugang Li","email":"","orcid":"","institution":"The 334 Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Pugang","middleName":"","lastName":"Li","suffix":""},{"id":341371334,"identity":"1fd19e79-6a87-4090-b9b7-92f80f1b6d68","order_by":2,"name":"Yi Sun","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Sun","suffix":""},{"id":341371335,"identity":"5245cae4-c971-46b6-8141-e19875cbb4a5","order_by":3,"name":"YaoZhou Shi","email":"","orcid":"","institution":"Affiliated Hosital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"YaoZhou","middleName":"","lastName":"Shi","suffix":""},{"id":341371338,"identity":"b3d27cb0-42f8-4425-8d32-16e469d2fcd4","order_by":4,"name":"Jing Bian","email":"","orcid":"","institution":"Affiliated Hosital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Bian","suffix":""},{"id":341371339,"identity":"c329669b-b2dd-494b-8945-bd0823e31ce8","order_by":5,"name":"Hua-Shuo Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACAwYGNiBlw8AgAeYzE60ljXQth0nQYs5//NmDHxXnE+fP7k6TYKiwTmxgP3sArxbLGQnphj1nbhszzjm7TYLhTHpiA09eAn6H3WA4JsHbdluOWSJ3mwRj2+HEBgkeA/xazh9sk/zbdo6HDazlHzFaDiSzSfO2HZDjAWtpIEbLjTQ2aZkzycYSErmbLRKOpRu38eQQctjxZ5JvKuwS58/I3XjjQ421bD/7GfxaUEECAySaRsEoGAWjYBRQCADjzEDQH3fZWQAAAABJRU5ErkJggg==","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hua-Shuo","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-08-16 14:27:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4925629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4925629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66555671,"identity":"49147249-f466-4451-a54c-6f6059fb0c98","added_by":"auto","created_at":"2024-10-14 09:27:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55118,"visible":true,"origin":"","legend":"\u003cp\u003eSample selection\u003c/p\u003e","description":"","filename":"Figure1Sampleselection.png","url":"https://assets-eu.researchsquare.com/files/rs-4925629/v1/ec1dd24ca7817dee1cbfd28d.png"},{"id":66555961,"identity":"cafb30ac-b0e4-4d98-bd5d-42ec193767c1","added_by":"auto","created_at":"2024-10-14 09:35:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61524,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive characteristics of the total anxiety,State Anxiety and Trait Anxiety.\u003c/p\u003e","description":"","filename":"Figure2DescriptivecharacteristicsofthetotalanxietyStateAnxietyandTraitAnxiety..png","url":"https://assets-eu.researchsquare.com/files/rs-4925629/v1/ee3bab8eb8eae84336f3e78d.png"},{"id":66555685,"identity":"eb4bd688-5467-4f57-853b-b4f867b10ae2","added_by":"auto","created_at":"2024-10-14 09:27:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47307,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of State Anxiety and Trait Anxiety.\u003c/p\u003e\n\u003cp\u003eNote: Numbers in the graph represent correlation coefficients. *p \u0026lt;0 .05; **p \u0026lt;0 .01;***p \u0026lt;0 .001.\u003c/p\u003e","description":"","filename":"Figure3CorrelationanalysisofStateAnxietyandTraitAnxiety.png","url":"https://assets-eu.researchsquare.com/files/rs-4925629/v1/2acf6d79abddd895f242c0a7.png"},{"id":66554090,"identity":"1a12b24f-590b-4f50-bd98-e90bacf48c66","added_by":"auto","created_at":"2024-10-14 09:19:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79227,"visible":true,"origin":"","legend":"\u003cp\u003eThe LGM analysis of the relationship between State Anxiety and Trait Anxiety.\u003c/p\u003e\n\u003cp\u003eNote: The red arrows represent positive covariances, while the blue arrows represent negative covariances. All arrows in the diagram are significant, and for the sake of simplification, standard deviations and p-values are not indicated.\u003c/p\u003e","description":"","filename":"Figure4TheLGManalysisoftherelationshipbetweenStateAnxietyandTraitAnxiety..png","url":"https://assets-eu.researchsquare.com/files/rs-4925629/v1/64a90a00ac6c9efe1e145ad0.png"},{"id":66557496,"identity":"cd62eb7b-3b16-4b6c-8d53-6e4e6880cd2b","added_by":"auto","created_at":"2024-10-14 09:51:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":761706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4925629/v1/abd30b6c-e007-4c02-873b-916e5808463b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Bivariate Latent Growth Model to Better Understand the Anxiety Symptom in Parkinson's Patients","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eParkinson's disease is the second-largest neurodegenerative disease globally, characterized by the primary pathological changes of degeneration and death of dopamine-producing neurons in the substantia nigra [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinically, the disease is predominantly characterized by motor symptoms such as tremors, bradykinesia, rigidity, and postural instability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition to motor symptoms, Parkinson's disease is accompanied by various non-motor symptoms, including sleep disturbances, autonomic dysfunction, cognitive impairment, anxiety, depression, apathy, etc [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Some non-motor symptoms and signs have been shown to manifest several years to decades before clinical diagnosis of Parkinson's disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The annual incidence of Parkinson's disease is approximately (4\u0026thinsp;~\u0026thinsp;20) per 100,000 individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is estimated that by 2040, nearly 13\u0026nbsp;million people worldwide will be affected by Parkinson's disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The disease poses numerous challenges to patients, reducing their quality of life, gradually diminishing their daily living capabilities due to motor impairments, leading to various negative emotions, and increasing the burden on both family members and healthcare systems.\u003c/p\u003e \u003cp\u003ePeople are familiar with Parkinson's disease (PD) primarily for its typical motor symptoms, but at least one-third of patients also suffer from anxiety disorders [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Anxiety is one of the earliest non-motor symptoms to appear in PD, emerging up to 20 years before the onset of motor symptoms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is characterized by symptoms such as lack of concentration, persistent worry, muscle tension, and an increase in tremor severity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The presence of anxiety exacerbates the condition and is closely associated with the severity of motor symptoms, decreased quality of life, increased disability, and mortality rates, posing challenges to the treatment management of Parkinson's disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research has found that even if individuals are not diagnosed with anxiety at the time of PD diagnosis, subsequent treatment or some symptoms of Parkinson's can induce anxiety [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Compared to those without anxiety, patients with both Parkinson's disease and anxiety exhibit greater disability and worse health conditions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Anxiety can be classified into state anxiety and trait anxiety [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. According to Spielberg's early formulation, anxiety is a unidimensional construct that includes both state and trait anxiety, seen as different sides of the same coin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. State anxiety is a transient response to events causing anxiety and changing situations from moment to moment. State anxiety is low when there is no danger or very little danger, while trait anxiety is a relatively stable tendency to react to experiences that induce anxiety [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Thus, trait anxiety is a relatively stable characteristic showing consistent individual differences in anxiety tendencies and can be considered a personality trait. There is a mutual influence between state anxiety and trait anxiety. Prolonged trait anxiety may increase the likelihood of experiencing state anxiety in specific situations, and vice versa. Different individuals may experience and express state anxiety and trait anxiety differently. Some individuals may be more prone to experiencing state anxiety through trait anxiety, while others may exhibit different reactions in various situations. Overall, the relationship between state anxiety and trait anxiety is complex and multi-layered. Understanding this relationship can contribute to a more comprehensive understanding of the nature of anxiety and provide more targeted approaches for intervention and treatment.\u003c/p\u003e \u003cp\u003eAnxiety has received relatively less attention in the literature, with a predominant focus on the domain of depression. Research on anxiety primarily emphasizes its mechanisms, treatment, and correlations with other symptoms, such as anxiety's associations with depression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], motor symptoms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], autonomic nervous system functioning [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and cognition [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The application of latent variable growth models (LGM) in the field of anxiety has been limited, mostly concentrating on studies involving adolescents [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To date, there has been no independent utilization of LGM to investigate the trajectory of anxiety in PD patients, possibly due to the relatively stable longitudinal nature of anxiety, where changes over several years may not be pronounced [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], resulting in less-than-ideal research outcomes. This study aims to explore the anxiety trajectory in Parkinson's disease patients, providing a deeper understanding of the dynamic evolution of anxiety within individuals. We categorize anxiety into trait anxiety and state anxiety, utilizing a bivariate latent growth model to investigate the developmental relationship between state anxiety and trait anxiety. Such research contributes to a more profound comprehension of the changing trajectory of anxiety, facilitating the development of more effective intervention measures to enhance individual mental well-being.\u003c/p\u003e"},{"header":"2 Subjects and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data and sample\u003c/h2\u003e \u003cp\u003e We obtained data from the Parkinson's Progression Markers Initiative (PPMI),a publicly available database. In 2010, The Michael J. Fox Foundation and a core group of academic scientists and industry partners launched the The Parkinson's Progression Markers Initiative (PPMI) aims to investigate much-needed biomarkers for the onset and progression of Parkinson's disease. Data used in the preparation of this article were obtained from the Parkinson\u0026rsquo;s Progression Markers Initiative (PPMI) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ppmi-info.org/access-data-specimens/download-data),RRID:SCR_00643\" target=\"_blank\"\u003ewww.ppmi-info.org/access-data-specimens/download-data),RRID:SCR_00643\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ppmi-info.org/access-data-specimens/download-data),RRID:SCR_00643\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e1. For up-to-date information on the study, visit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ppmi-info.org/access-data-specimens/download-data),RRID:SCR_00643\" target=\"_blank\"\u003ewww.ppmi-info.org.This\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ppmi-info.org.This\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e study includes data from PD patients in the PPMI database from 2010 to 2024, which was visited at 12-month intervals. Data with missing basic demographic information and missing rates greater than 20% were excluded. The last six data counts were left, the highest rate of missing data was 14.7%, and the number of samples was 475 participants.The sample selection process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. None of our participants received treatment at baseline, but underwent confirmative assessments, including clinical and cognitive evaluations, imaging examinations, and biological sampling, which were approved by the local participant Central Institutional Review Board. All participants provided written informed consent prior to enrollment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cp\u003eThe State-Trait Anxiety Inventory (STAI) is a widely used psychological assessment tool for measuring anxiety levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It consists of two component scales: State Anxiety and Trait Anxiety. These two scales can be used independently to assess state and trait anxiety, respectively. The STAI questionnaire comprises 40 items, with each subscale containing 20 items. Participants are evaluated on their levels of state and trait anxiety based on their responses to these items. Responses are typically rated on a 4-point scale reflecting the frequency of experiences during a specific period: \"Almost Never (1),\" \"Sometimes (2),\" \"Often (3),\" and \"Almost Always (4)\". The total scores for trait and state anxiety range from a minimum of 20 to a maximum of 80, with higher scores indicating higher levels of anxiety in the respective domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eFirst, descriptive statistics were performed on the data, with continuous variables represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical variables by frequency counts (%). Next, Pearson correlation analysis was used to explore the relationships between variables. To understand the trajectory of the variables, we made two assumptions based on previous research results: first, that the trajectory of the variable is a no-growth model, and second, that the trajectory of the variable is a linear growth model. The best model was then selected based on fit indices. The linear growth model includes two latent variables: the intercept factor and the slope factor. The LGM uses the mean and variance parameters of these latent variables to describe within-group and between-group differences. Specifically, the mean of the intercept factor represents the average initial state, while the variance of the intercept factor indicates the degree of individual differences at a specific time point. The greater the variance, the more significant the initial differences between individuals. The mean of the slope factor represents the average growth rate between time points, and the variance of the slope factor reflects the magnitude of individual differences in growth rates. The bidirectional arrow between these two factors indicates their correlation. To further explore the relationship between state anxiety and trait anxiety, this study employed the bivariate latent growth curve model, which combines two latent growth models, with the parameter interpretations being the same as mentioned above. This model is widely used to study the dynamic relationships between two variables over time, as well as their covariation, interactions, and individual differences. It is particularly useful for long-term studies aiming to understand the complex dynamic relationships between variables. This study used the bivariate latent growth curve model to examine the longitudinal patterns and correlations between state anxiety and trait anxiety.\u003c/p\u003e \u003cp\u003eFor the missing data, we use Maximum likelihood (ML) estimation to estimate the parameters of the model. To assess the model fit degree, we rely on Comparative Fit Index (CFI), Tucker-Lewis Index (TLI) and Standardized Root Mean Square Residual(SRMR) values, as the χ2 goodness-of-fit statistic can be overly sensitive for large sample sizes, and therefore, we do not employ it. For CFI and TLI,a Value at 0.8\u0026thinsp;~\u0026thinsp;0.9 represent generic model fits and are also acceptable, a value above 0.90 is considered acceptable, and a value exceeding 0.95 indicates a good fit. SRMR examines the fit of the model by the size of the residue, and its values range from 0 to 1 and indicate a good model fit when the value is less than 0.08.\u003c/p\u003e \u003cp\u003eTo implement the necessary model and conduct the analysis, we utilize Mplus 8.9. Descriptive analysis and plotting are performed using SPSS version 25.0 and R (version 4.2.3). The test level is set at a p-value of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic information\u003c/h2\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be seen that there are 337 participants (70.9%) in our sample who are aged 56 and above, with 178 females (37.5%) and 297 males (62.5%). The majority of the sample is White, accounting for 443 participants (93.3%), and the education level is concentrated between 13\u0026ndash;23 years (78.9%). The Hoehn and Yahr stages are mainly concentrated in stages 1 and 2, with a total of 463 participants (97.5%). The average age at onset is 59.02\u0026thinsp;\u0026plusmn;\u0026thinsp;9.95 years old, and the average disease duration is 1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistics(N\u0026thinsp;=\u0026thinsp;475)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;56 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138(29.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u0026thinsp;~\u0026thinsp;65 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143(30.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194(40.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178(37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297(62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;13years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(19.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13-23years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e375(78.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;23years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e443(93.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoehn and Yahr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estage0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estage1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192(40.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estage2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271(57.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003estage3-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(2.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170(35.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305(64.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at PD Symptom Onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.02\u0026thinsp;\u0026plusmn;\u0026thinsp;9.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration from PD Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that, with the progression of the disease, there is little change in the total anxiety score as well as the scores for state anxiety and trait anxiety. This further validates previous research findings [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, through Pearson correlation analysis, describes the relationship between trait anxiety and state anxiety in PD patients, showing a general positive correlation between the two. Despite observing minimal changes in the scores of trait anxiety and state anxiety through statistical description, it is essential to explore the correlation between these two aspects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eNumbers in the graph represent correlation coefficients. *p\u0026thinsp;\u0026lt;\u0026thinsp;0 .05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0 .01;***p\u0026thinsp;\u0026lt;\u0026thinsp;0 .001.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate Latent Growth Curve Model\u003c/h2\u003e \u003cp\u003eBased on the results of statistical descriptions, we constructed both non-growth models and linear growth models for anxiety, trait anxiety, and state anxiety separately, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e From the results, it can be observed that the fit indices of the linear growth models for all variables are superior to those of the non-growth models. Therefore, we chose the linear growth model to examine the patterns of change and individual differences in anxiety, trait anxiety, and state anxiety. We utilized univariate latent growth curve models to explore the developmental trajectories of each variable, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e The results from the analysis of univariate latent growth curve models align with the descriptive statistics depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, affirming a strong fit of the LGM to the dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit Indices of Latent Growth Models for Each Variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX2/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.149/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.278/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103.537/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.052/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eState Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.749/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.032/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter Estimates of Univariate Latent Growth Curve Model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003evariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003evariance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257.927***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.311***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;7.286*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.030***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.822***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;2.378*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.931***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.651***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;2.173*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote:*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01;***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcerning total anxiety scores, the intercept mean is 66.215 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), accompanied by a variance of 257.927 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates an initial anxiety score level of 66.215, with noteworthy variability among individuals in their initial state. The slope mean is 0.122 (P\u0026thinsp;=\u0026thinsp;0.434), insignificantly suggesting no overall linear increase or decrease in total anxiety scores. Nevertheless, the significant variance of the slope implies substantial individual variability, indicating potential significant differences in growth trends despite the mean being nonsignificant. The correlation between the intercept and slope of total anxiety scores is -7.286 (P\u0026thinsp;=\u0026thinsp;0.045), signifying a negative correlation at the individual level. This might indicate that, in certain individuals, higher initial states could correspond to smaller growth.\u003c/p\u003e \u003cp\u003eFor state anxiety, the intercept mean is 33.030 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), coupled with a variance of 64.822 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests an initial state anxiety level of 33.030, with significant variability among individuals in their initial state. The slope mean is -0.056 (P\u0026thinsp;=\u0026thinsp;0.528), nonsignificantly indicating no overall linear increase or decrease in state anxiety scores. Nonetheless, the significant variance of the slope implies considerable individual variability, suggesting potential significant differences in growth trends despite the mean being nonsignificant. The correlation between the intercept and slope of state anxiety scores is -2.378 (P\u0026thinsp;=\u0026thinsp;0.048), revealing a negative correlation at the individual level. This may suggest that, in certain individuals, higher initial states could correspond to smaller growth.In the latent growth model for state anxiety, we found that the linear growth model fits better than the no-growth model, although the slope factor is not significant. We analyzed this situation and identified several possible reasons: low variability in the slope factor, meaning that individual changes might not be large enough to reach statistical significance; insufficient measurement time points, where the distribution or number of time points might not be adequate to capture significant linear changes. We considered the model's fit indices, theoretical foundation (anxiety symptoms in Parkinson's patients tend to worsen gradually), explanation, and prediction. Despite the non-significance of the slope factor, the linear growth model's fit indices are significantly better than those of the no-growth model, and it holds theoretical and practical significance. Therefore, choosing the linear growth model is reasonable.\u003c/p\u003e \u003cp\u003eFor trait anxiety, the intercept mean is 33.176 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the slope is 0.177 (P\u0026thinsp;=\u0026thinsp;0.030). This indicates an initial trait anxiety level of 33.176, with a slight linear upward trend in trait anxiety scores over the six follow-up times. The annual increase in trait anxiety scores is approximately 0.117 points, indicating a worsening of patients' trait anxiety symptoms each year. Additionally, the variance of the intercept is 74.931 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the variance of the slope is 1.651 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), both of which are significant, indicating individual differences in the initial level and rate of increase of trait anxiety. The correlation between the intercept and slope of trait anxiety scores is -2.173 (P\u0026thinsp;=\u0026thinsp;0.022), suggesting that individuals with higher initial states may experience smaller growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Bivariate latent growth curve model\u003c/h2\u003e \u003cp\u003eTo delve deeper into the relationship between state anxiety and trait anxiety, we conducted a bivariate latent growth curve model analysis. The results indicated a good model fit, with a chi-square/degrees of freedom ratio of 796.496/56, CFI (Comparative Fit Index) of 0.859, TLI (Tucker-Lewis Index) of 0.834, and SRMR (Standardized Root Mean Square Residual) of 0.044.The interaction effects in the path analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and for a more intuitive understanding of these interactions, refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All P-values in the figure are greater than 0.05, allowing us to reject the hypothesis of a covariance of 0. This leads to the conclusion that all intercept and slope factors have significant associations. Specifically, the slope of trait anxiety is positively correlated with the slope of state anxiety, as well as the intercept of trait anxiety being positively correlated with the intercept of state anxiety. the growth trend in trait anxiety is positively correlated with the growth trend in state anxiety, indicating that individuals with a higher growth trend in trait anxiety are more likely to experience a higher growth trend in state anxiety at a certain time point. The positive correlation coefficient between the intercepts suggests a positive relationship at the individual level, indicating that individuals with higher scores in trait anxiety are more likely to experience higher levels of state anxiety at a certain time point. The remaining covariances between intercepts and slopes are negative, indicating a negative dynamic relationship between these two concepts. This suggests that in some individuals, those with higher initial states may experience smaller growth, for example, individuals with higher levels of trait anxiety may show lower growth in state anxiety.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter estimates from the LGM analysis of the relationship between State Anxiety and Trait Anxiety.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor covariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITRA with ISTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITRA with SSTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTRA with ISTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTRA with SSTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTRA with ITRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSTA with ISTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ISTA: Intercept of State Anxiety; SSTA: Slope of State Anxiety; ITRA: Intercept of Trait Anxiety; STRA: Slope of Trait Anxiety; SE:standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe red arrows represent positive covariances, while the blue arrows represent negative covariances. All arrows in the diagram are significant, and for the sake of simplification, standard deviations and p-values are not indicated.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study employed a bivariate latent basic growth curve model to explore the relationship between changes in state anxiety and trait anxiety. Initially, a univariate latent growth curve model was utilized to examine the developmental trajectories between the two variables, revealing subtle changes. The longitudinal process of anxiety was observed to be relatively stable, as indicated by the heatmap, suggesting a correlation between the two and sparking further curiosity for exploration. Subsequently, a bivariate latent growth curve model was employed to investigate the relationship between state anxiety and trait anxiety. The analysis results indicated a significant correlation in the changes of trait anxiety and state anxiety over time among Parkinson's patients. At the individual level, those exhibiting a higher growth trend in trait anxiety were more likely to experience a higher growth trend in state anxiety at a specific time point. Individuals scoring higher in trait anxiety were also more likely to experience higher levels of state anxiety at a particular time point, and vice versa. Evidence suggests that individuals with high trait anxiety are prone to developing stress-induced depression or anxiety disorders due to heightened stress responsiveness, increased passive coping responses to environmental challenges, cognitive function changes, and decreased social competitiveness [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], aligning with the findings of our study. Negative covariances between intercepts and slopes indicate a negative dynamic relationship between these two concepts. This may suggest that in some individuals, those with higher initial states may experience smaller growth, such as individuals with higher levels of trait anxiety potentially showing lower growth in state anxiety, or vice versa. The intimate correlation and mutual influence between trait anxiety and state anxiety are evident. Conventional state anxiety is a normal response to sudden situations and stressful circumstances. However, prolonged and intense anxiety, if not alleviated, may lead to self-suppression, causing long-term damage to both psychological and physiological health [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Trait anxiety, being more stable, is often challenging to improve rapidly, influenced by factors such as individual physiology and genetics. State anxiety is typically short-term and associated with specific situations or events. Once the situation eases, state anxiety may alleviate. Therefore, focusing on improving state anxiety appears to be a more direct and effective approach. As state anxiety improves, it impacts the accumulation of trait anxiety, thereby reducing trait anxiety. The decrease in trait anxiety, in turn, contributes to the reduction of state anxiety, creating a cyclic process of continual reduction.\u003c/p\u003e \u003cp\u003eStudies have shown that dysregulation of the locus coeruleus in the brain can lead to anxiety [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This is because the nerve endings of norepinephrine neurons are highly concentrated in the locus coeruleus, accounting for 70% of the extracellular norepinephrine in the brain. Norepinephrine is a major monoamine neurotransmitter responsible for arousal, alertness, concentration, and activating the stress response [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, when the locus coeruleus is dysregulated, it can lead to anxiety. Research has also indicated that the loss of serotonergic neurons can lead to anxiety symptoms [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Serotonin consists of 14 types of receptors, with serotonin playing a crucial role in the etiology of anxiety. Serotonin is a neurotransmitter that plays a vital role in regulating mood, fear, aggregation, and modulating anxiety [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The dysfunction of the hypothalamic-pituitary-adrenal axis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and the gamma-aminobutyric acid system [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] is also significantly related to anxiety. Research on the mechanisms of anxiety is gradually advancing, which is highly beneficial for the treatment of anxiety. Currently, the primary methods for treating anxiety include medication and non-pharmacological interventions. Medication options encompass the use of Benzodiazepines, tricyclic agents, and other antidepressants [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Non-pharmacological treatment approaches involve Cognitive Behavioral Therapy (CBT), Transcranial Magnetic Stimulation (TMS), and Deep Brain Stimulation (DBS) [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, existing pharmacological treatments are insufficient to alleviate the complications of Parkinson's disease. Hence, alternative treatment options, such as yoga, aromatherapy, and music therapy, are being explored [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In comparison to specialized treatments targeting state anxiety, personally acquiring effective coping and management methods might be more accessible. For instance, learning effective stress management techniques, such as time management, problem-solving, and setting realistic goals, can contribute to reducing state anxiety. Employing relaxation techniques, such as deep breathing, progressive muscle relaxation, or meditation, when facing tense situations can help alleviate physical tension. Taking control of one's emotions, acquiring effective emotion regulation skills, seeking support, and maintaining a positive attitude can also contribute to reducing state anxiety. Additionally, seeking help from friends, family, or professional mental health experts when faced with challenging situations is a viable option. Evidence suggests that more severe symptoms of depression or anxiety can deteriorate social functioning and impact daily life activities [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Therefore, improving anxiety symptoms can also enhance the social functioning and daily living activities of Parkinson's disease patients. Anxiety symptoms are also associated with motor function [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], depression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], sleep disorders [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], cognitive impairment [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and autonomic dysfunction [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Addressing and treating these symptoms can also alleviate anxiety symptoms. This indicates that we can consider treating the anxiety symptoms of Parkinson's patients from this perspective.\u003c/p\u003e \u003cp\u003eThis study also has some limitations: First, due to the complex overlap of anxiety with depression, autonomic dysfunction, and PD-related motor symptoms [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], it is often challenging to identify and lacks well-designed studies and PD-specific tools. This highlights a direction for future research and development. Second, Self-reported measures of anxiety and potential biases in PD patients may limit the value of this study. Third, the predominantly Caucasian participant group may limit the generalizability of our study results. Fourth, anxiety is a relatively stable process, and although our fit indices (CFI, TLI) fall within an acceptable range, they are not very high. Thus, expanding the sample size, increasing the frequency of follow-ups, and conducting a more precise examination of the trajectory of anxiety are warranted. Additionally, considering other models from different perspectives could provide a more comprehensive understanding of anxiety in individuals with Parkinson's disease.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eBased on the analysis of 6-year longitudinal data from the PPMI, our study elucidates the developmental trajectories of state anxiety and trait anxiety. Furthermore, using a bivariate latent growth curve model, we reveal the interactions between state anxiety and trait anxiety. Our findings suggest that at the individual level, those with a higher growth trajectory in trait anxiety are more likely to experience a higher growth trajectory in state anxiety at a specific point in time. Individuals scoring higher on trait anxiety are also more likely to experience higher levels of state anxiety at a particular time point. Individuals with higher initial states may undergo smaller growth, indicating that those with higher levels of trait anxiety may exhibit lower growth in state anxiety, and vice versa. The intimate correlation and mutual influence between trait anxiety and state anxiety highlight the interconnectedness of these two constructs. Considering the interplay between them, interventions aimed at improving state anxiety in Parkinson's patients may contribute to reducing trait anxiety, enhancing overall mental health, and preventing them from becoming individuals prone to anxiety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding: PPMI \u0026ndash; a public-private partnership \u0026ndash; is funded by the Michael J. Fox Foundation for Parkinson\u0026rsquo;s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson\u0026apos;s, AskBio, Avid Radiopharmaceuticals, BIAL, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003econtributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQW contributed to the conception, design, data analysis, and interpretation of the study. She conducted literature review, data collection, and manuscript drafting. JB YS and YS were involved in data collection and preprocessing. PL and HZ played crucial roles in the design and conceptualization of the study, as well as providing essential guidance and supervision throughout the entire research process. Collectively, all authors reviewed and contributed to the manuscript\u0026apos;s development, ensuring accuracy and scientific rigor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Social Development Project of Xuzhou City (Number: KC21267).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParkinson\u0026rsquo;s Progression Markers Initiative (PPMI) data is available at www.ppmi-info.org/access-data-specimens/download-data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Competing interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not require ethics approval because the data is publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of the manuscript in BMC Geriatrics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJenner P, Olanow CW (2006) The pathogenesis of cell death in Parkinson's disease. 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J Neuropsychiatry Clin Neurosci 28(3):211\u0026ndash;216\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 disease1, Bivariate Latent Growth Model2, Trait Anxiety3, State Anxiety4, Longitudinal study5","lastPublishedDoi":"10.21203/rs.3.rs-4925629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4925629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study utilizes the Bivariate Latent Growth Model to explore the developmental trajectories of trait anxiety and state anxiety, as well as the interrelationships between the trait anxiety and state anxiety.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We obtained six-year follow-up data from 475 Parkinson's disease patients through the Parkinson's Progression Markers Initiative. We employed latent growth models to explore the trajectories of anxiety, trait anxiety, and state anxiety. Subsequently, we used the Bivariate Latent Growth Model to investigate the longitudinal relationships between state anxiety and trait anxiety.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe trajectories of anxiety, trait anxiety, and state anxiety were best described by a linear growth model. The intercept and slope of each were significantly correlated with the intercept, and the variance of both intercepts and the correlation between them were all significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Only the slopes of the total anxiety score and state anxiety were not significant, but the variance of their slopes was significant, indicating significant variability among individuals. The variance of the trait anxiety slope was also significant. The results of the Bivariate Latent Growth Model show significant associations among all intercept and slope factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.018). Specifically, the intercept of trait anxiety is positively correlated with the intercept of state anxiety, and the slope of trait anxiety is positively correlated with the slope of state anxiety. The remaining path covariances between intercepts and slopes are negative.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur research results indicate that among individuals with Parkinson's disease, those showing a higher growth trend in trait anxiety are more likely to experience a higher growth trend in state anxiety at a particular time point. Individuals scoring higher on trait anxiety are more likely to experience elevated levels of state anxiety at a specific time point. Individuals with higher initial levels may undergo smaller growth. For instance, individuals with higher levels of trait anxiety may exhibit lower growth in state anxiety or vice versa. It is evident that there is a close and reciprocal relationship between trait anxiety and state anxiety, with mutual influences.\u003c/p\u003e","manuscriptTitle":"Using Bivariate Latent Growth Model to Better Understand the Anxiety Symptom in Parkinson's Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 09:18:57","doi":"10.21203/rs.3.rs-4925629/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":"f2c674f2-bfa9-458d-9d1c-f31a14780249","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T09:19:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 09:18:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4925629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4925629","identity":"rs-4925629","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}