Dysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity

Dysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity | 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 Dysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity Tabarek Hadi Al-Naqeeb, Hussein Kadhem Al-Hakeim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7919099/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 Background Dysregulation of Wnt signaling and neuroinflammation are critically implicated in Parkinson’s disease (PD) pathogenesis. This study investigates the clinical utility of key circulating biomarkers related to these pathways for diagnosing PD and correlating with symptom severity. Methods In this case-control study, 90 PD patients and 45 healthy controls (HC) were recruited. Serum levels of Wnt-related proteins (DKK1, Sclerostin, RSPO1), HMGB1, and electrolytes were measured using ELISA and colorimetric assays. Participants underwent comprehensive motor (MDS-UPDRS, Hoehn & Yahr) and non-motor (Fibro-Fatigue, NMS Scale) assessments. Statistical analyses included multivariate general linear models, partial correlations, binary logistic regression, and receiver operating characteristic (ROC) analysis. Results Serum levels of DKK1 and HMGB1 were significantly elevated in the PD group compared to HC (p < 0.001). Binary logistic regression identified both as independent predictors of PD (DKK1: OR = 1.280, p < 0.001; HMGB1: OR = 1.000, p = 0.003). ROC analysis confirmed their strong diagnostic accuracy (DKK1 AUC = 0.81; HMGB1 AUC = 0.72). Within the PD cohort, HMGB1 correlated with disease progression (Hoehn & Yahr: r = 0.424, p < 0.01), while RSPO1 correlated with worse motor experiences of daily living (r = 0.286, p = 0.010) and Sclerostin with motor complications (r = 0.229, p = 0.033). However, no biomarker predicted severe motor or non-motor symptom severity in dedicated regression and ROC models. Conclusion DKK1 and HMGB1 are robust diagnostic biomarkers for PD, underscoring the roles of Wnt dysregulation and neuroinflammation. The correlation of Sclerostin and RSPO1 with specific symptom domains suggests their function as disease modulators rather than diagnostic markers. This panel differentiates between biomarkers for diagnosis and those associated with symptom expression. Parkinson’s disease Wnt pathway Neuroinflammation Motor symptoms Nonmotor symptoms Figures Figure 1 Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder that extends beyond its classic motor symptoms of tremor, rigidity, and bradykinesia ( 1 ). Non-motor symptoms, including cognitive impairment, often precede motor onset by years and significantly shape disease progression and quality of life ( 2 ). The pathophysiological hallmarks of PD include the aggregation of alpha-synuclein into Lewy bodies and the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to striatal dopamine deficiency ( 3 , 4 ). Current treatments, such as levodopa and deep-brain stimulation, offer symptomatic relief but do not delay disease progression, highlighting an urgent need for novel therapeutic targets and predictive biomarkers ( 5 ). Among the less-explored pathways in PD, the Wingless/integrated-1 (Wnt)/β-catenin signaling cascade has emerged as a key regulator of neurodevelopment, synaptic plasticity, and adult neurogenesis ( 6 , 7 ). The Wnt/β-catenin signaling pathway, a critical regulator of embryonic development and adult tissue homeostasis, has emerged as a promising area of investigation in neurodegeneration ( 6 , 7 ). The Wnt/β-catenin signaling pathway is an important cellular signaling pathway involved in various biological processes such as apoptosis, proliferation, fibrogenesis, homeostasis, differentiation, growth, and repair ( 8 ). In the central nervous system, this pathway supports neurogenesis, synaptic plasticity, and neuronal survival ( 9 ). Importantly, its activation can protect dopaminergic neurons by improving mitochondrial function and reducing oxidative stress ( 9 , 10 ). Studies have shown that the Wnt/β-catenin pathway can regulate mitochondrial function in PD, maintaining mitochondrial membrane potential, altering mitochondrial morphology, and reducing the release of reactive oxygen species (ROS), thus protecting dopaminergic neurons ( 10 ). Endogenous agonists and antagonists precisely modulate the pathway. Dickkopf-1 (DKK1) is a potent secreted antagonist that inhibits Wnt/β-catenin signaling by binding to the LRP5/6 co-receptor ( 11 ), and its upregulation is linked to neuronal death guidance ( 12 ). Conversely, R-spondin 1 (RSPO1) acts as an agonist that can potentiate Wnt signaling and attenuate DKK1-mediated inhibition signaling ( 6 , 13 ). Concurrently, disruptions in cellular homeostasis are implicated in PD pathogenesis. The alarmin High Mobility Group Box 1 (HMGB1), when released extracellularly, acts as a potent mediator of neuroinflammation, and its levels are elevated in PD patients ( 14 ). Upon extracellular release, HMGB1 orchestrates inflammatory cascades, immunological responses, and drives neurodegeneration and cardiovascular complications ( 15 ). The serum HMGB1 level of PD patients was higher than that of the controls ( 16 ). HMGB1 can bind to alpha-synuclein, potentially catalyzing the neurodegenerative process ( 17 ). Furthermore, calcium homeostasis is critical for substantia nigra dopaminergic neurons, regulating functions from excitability to energy production ( 18 ). Defects in calcium processing are known to play an essential role in aging and neurodegeneration, and alpha-synuclein aggregation can directly disrupt calcium homeostasis, creating a vicious cycle of toxicity ( 19 ). Calcium ions are regulated by various pathways ( 20 ). α -synaptic nucleoprotein aggregation, an important pathologic feature of PD, leads to disruption of calcium homeostasis ( 21 ). While the roles of Wnt signaling, neuroinflammation, and ionic imbalance have been studied in isolation, their interplay in predicting PD symptomatology remains largely unexplored. Therefore, this study aims to investigate the potential of a panel of circulating biomarkers, including key regulators of the Wnt/β-catenin pathway (DKK1, RSPO1), and key mediators of neurodegeneration like HMGB1 and divalent cations (calcium and magnesium) in the context of PD symptomatology, as predictors for the development and severity of motor and non-motor symptoms in PD. Subjects and methods Participants The present case-control study examined a group of ninety patients diagnosed with PD and a control group of forty-five healthy individuals. The specimens were collected from Al-Sadr Medical City, Al-Najaf Teaching Hospital, and Al-Furat Al-Awsat Center for Neurosciences in Najaf city, Iraq, during the period from February to May 2025. The assessment of patients was carried out by taking a complete medical history and clinical examination. The diagnosis of PD was carried out using the UK Parkinson's Disease Society Brain Bank Clinical Diagnostic Criteria ( 22 ). All patients had bradykinesia along with a minimum of one other cardinal symptom (resting tremor, stiffness, or postural instability) and showed no characteristics indicative of an alternative parkinsonian syndrome ( 22 ). For the motor assessment, we used Part III (Motor Examination) of the MDS-UPDRS to rate the severity of MS ( 23 ). To maintain consistency, patients were assessed in the 'OFF' drug state, after a minimum 12-hour cessation of all dopaminergic medicines. A combination of tools was used to describe the severity and burden of non-MS. The NMS scale was used for a thorough assessment ( 24 ). The Hoehn and Yahr staging method was also used to rate how bad the epidemic was across the world ( 25 ). The same qualified neurologist gave all of the clinician-rated scales. The neurologist explored the presence of any systemic disease that may affect the studied parameters, especially liver disease and kidney disease, which were excluded from the study. In order to rule out the possibility of any overt systemic inflammation, the serum C-reactive protein (CRP) levels in all of the samples came back negative, coming in at less than 6 mg/l ( 26 ). The control and patient participants were required to give written consent before participating in the study. They were provided with detailed information beforehand. The study was granted approval by the Institutional Ethics Committee of the University of Kufa (MEC-110/2025). The study followed ethical and privacy laws both in Iraq and internationally. It complied with various international guidelines and declarations, such as the World Medical Association's Declaration of Helsinki. Clinical measurements An experienced neurologist conducted a semi-structured interview to evaluate and gather sociodemographic and clinical information from control subjects and patients. An expert in neurology assessed the extent of the motor and MS associated with PD by employing the Movement Disorders Society Revision of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) as outlined previously ( 27 ). The MDS-UPDRS is divided into four parts: Part I: non-motor Experiences of Daily Living (nM-EDL), Part II: motor Experiences of Daily Living (m-EDL), Part III: motor examination, and Part IV: motor complications ( 27 ). Neurologists conducted the MDS-UPDRS assessment on patients, utilizing the ratings from the four domains for statistical analysis. The severity of CFS and fibromyalgia was assessed by a senior psychiatrist using the Fibro-Fatigue scale ( 28 ). Assays A volume of 5 milliliters of fasting blood samples was collected at approximately 9:00 a.m. After complete clotting, the blood samples were centrifuged at 1,200 × g for five minutes at room temperature. Subsequently, the serum was carefully divided and distributed among three Eppendorf tubes. Excluded from the study were samples that had undergone hemolysis. The tubes were subsequently frozen at -80°C and remained in this state until they were thawed for the assays. We utilized sandwich ELISA techniques to quantify the levels of HMGB1, RSPO1, DKK1, and Sclerostin using ELISA kits provided by Wuhan USCN Business Co., Ltd. (China). Albumin, T.Mg, Ionized Mg, T.Ca, Ionized Ca, T.Ca/Mg and Ionized Ca/Mg were measured in serum using colorimetric kits from Spectrum® in Cairo, Egypt. The coefficient of variation (CV) for all ELISA kits was less than 10.0%. We employed sample dilutions for samples that contained analytes with elevated concentrations by 1:5 with the sample diluent provided with the kit. Statistical analysis Based on the statistical distribution, the analysis's findings divided the variables into two groups: normally distributed and nonparametric variables identified by the results of the Kolmogorov-Smirnov test. A normal distribution of the results was given as mean ± SD. The nonparametric variables' values are shown as medians and 25%–75% percentiles. The comparison between the patient and control groups was done using the Mann-Whitney U-test for non-normally distributed variables, and one-way analysis of variance was used to compare scale variables across groups distributed normally. The contingency tables (χ 2 tests) assessed the associations between categorical variables. Pearson's product-moment correlation was used to analyze the relationships between scale variables and the biomarkers after transforming non-normally distributed variables into the Ln transformation. Using multivariate general linear model (GLM) analysis (followed by tests of between-subject effects), the associations between categories and biomarkers were investigated, accounting for confounding variables including age, sex, and body mass index (BMI). From the multivariate GLM, we computed the estimated marginal mean of variables after controlling for all covariates. We performed a binary logistic regression analysis using the diagnosis of severe versus moderate MS and NMS in the PD group, with biomarkers serving as explanatory variables. The odds ratio with 95% confidence intervals was calculated, along with the predictive accuracy, sensitivity, and specificity. The latter was utilized to estimate the effect size of the model. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic efficacy of the identified biomarkers for the diagnosis of severe fatigue in PE patients. The concentration cut-off values, determined by the area under the curve (AUC), provide optimal sensitivity and specificity. Confidence intervals were also determined to evaluate the precision of the calculated AUC; a narrower interval signifies a more certain conclusion. A higher outcome for Youden's J statistic indicates that the biomarker increases with diagnosis. We used the concentration that aligned with the maximum Youden's J statistic as the cut-off values. In this study, statistical significance was established at a p-value of 0.05 using two-tailed tests. IBM SPSS 26 for Windows was utilized to analyze the data. G*Power 3.1.9.7 showed that the a priori estimated sample size was 134, given a power of 0.90, alpha = 0.05, and effect size = 0.28. Results Sociodemographic and clinical data The results of sociodemographic and clinical data are presented in Table 1 In the current study, comparison between PD patients and healthy controls revealed no statistically significant differences in age, BMI, sex distribution, residency, exercise, TUD, and marital status. PD patients had significantly lower education levels, possibly indicating reduced health awareness and less cognitive stimulation. More PD patients were unemployed or retired, likely due to early symptoms affecting work ability. Family history is significantly higher in PD patients than in controls. The PD group exhibited a moderate level of illness severity, as shown by a mean Hoehn & Yahr stage of 2.772 ± 0.868. The UPDRS measured MS and gave the following scores: Part III (motor examination) was 54.267 ± 11.38, and Part IV (motor complications) = 16.767 ± 4.105. PD patients were taking medication: 70% (63/90) were using Levodopa, 27.8% (25/90) were taking Kemadrin (procyclidine), and 25.6% (23/90) were taking Sinemet (a mix of carbidopa and levodopa). Differences in the biomarkers between PD patient groups and controls The results in Table 2 demonstrated significant differences in serum neuronal damage biomarkers between HC and PD patients. Notably, DKK1 and HMGB1 levels were markedly elevated in the PD group compared with HC, with highly statistically significant (p < 0.001). The findings revealed no significant differences in sclerostin (p = 0.204) and RSPO1 (p = 0.371) levels among the two groups. Correlation matrix between the neuropsychiatric and clinical scores and the measured biomarkers Table 3 displays the partial correlation matrix between serum biomarkers and clinical scores among Parkinson’s disease (PD) patients. LnHMGB1 showed a positive correlation with Hoehn & Yahr stage (r = 0.424, p < 0.01), indicating its potential role as an inflammatory marker linked to disease progression. LnRSPO1 was positively correlated with both Total-FF (r = 0.285, p = 0.010) and TOTAL-m-EDL (r = 0.286, p = 0.010) performance. Sclerostin was positively associated with TOTAL Part IV scores (r = 0.229, p = 0.033),, total magnesium demonstrated a negative correlation with TOTAL-m-EDL (r=–0.276, p = 0.013), whereas total calcium was positively correlated with Total-FF (r = 0.223, p = 0.036), In contrast, other biomarkers, including LnDKK1, albumin, ionized calcium, and calcium-to-magnesium ratios (T.Ca/Mg and I.Ca/Mg)—did not show significant correlations with any of the clinical parameters assessed. Binary logistic regression analyses Table 4 presents the results of the binary logistic regression analysis used to evaluate the predictive value of circulating biomarkers in distinguishing Parkinson’s disease (PD) patients from healthy controls. Among the analyzed markers, DKK1 and HMGB1 emerged as significant predictors. DKK1 showed a strong positive association with PD diagnosis (B = 0.247, SE = 0.058, Wald = 18.297, p < 0.001), with an odds ratio (OR) of 1.280 (95% CI: 1.143–1.434), indicating that elevated serum DKK1 levels may reflect underlying neurodegenerative processes linked to Wnt signaling dysregulation. HMGB1 was also significantly associated with PD (B = 0.001, SE = 0.001, Wald = 9.042, p = 0.003), albeit with an OR of 1.000 (95% CI: 1.000–1.001), suggesting that even minimal changes in HMGB1 levels might contribute to disease risk through inflammatory mechanisms. The overall model demonstrated strong diagnostic performance, with 74% sensitivity and 90% specificity, and was statistically significant (χ² = 63.564, df = 4, R² = 0.522, p < 0.001).In contrast, RSPO1 (p = 0.518) and sclerostin (p = 0.877) did not show significant predictive value for PD. Furthermore, when evaluating the ability of biomarkers to distinguish between severe and moderate non-motor symptoms, no significant predictors were identified. Similarly, the model assessing motor symptom severity showed no significant findings. Prediction of PD vs. healthy controls The results of receiver operating characteristic-area under the curve (AUC) analysis of the biomarkers for predicting severe motor and non-motor in PD patients are presented in Table 5 and Fig. 1 . Among the biomarkers, DKK1 demonstrated the highest predictive performance with an AUC of 0.81 (95% CI: 0.74–0.89, p < 0.001), followed by HMGB1 with an AUC of 0.72 (95% CI: 0.62–0.82, p < 0.001). These results indicate that higher circulating levels of DKK1 and HMGB1 are associated with PD, supporting their potential utility as diagnostic biomarkers. RSPO1 and calcium parameters (total and ionized) showed moderate predictive ability, whereas other markers such as Sclerostin, Albumin, Mg, and Ca/Mg ratios were not useful (AUC 0.05). Prediction of severe non-motor symptom s Only RSPO1 showed weak predictive ability (AUC = 0.63, p = 0.036), while all other biomarkers had AUC values below 0.50, indicating no significant discrimination. This suggests that the examined biomarkers are largely insufficient for predicting the severity of non-motor symptoms in PD. Prediction of severe motor symptoms All biomarkers demonstrated AUC values below 0.50 (p > 0.05), indicating no predictive value for distinguishing severe versus moderate motor symptoms. Discussion The present study provides compelling evidence for the involvement of specific Wnt signaling modulators and inflammatory mediators in PD, differentiating between their utility as diagnostic biomarkers and their correlation with symptom severity. The central finding is the highly significant elevation of serum DKK1 and HMGB1 in PD patients compared to healthy controls, positioning them as key players in the disease's pathophysiology. The increase in serum levels of DKK1 and HMGB1 in PD is primarily attributed to neuroinflammatory processes and the pathological aggregation of proteins. HMGB1 is actively secreted by inflammatory cells and passively released by necrotic cells, playing a significant role in neuroinflammation, autophagy modulation, and apoptosis regulation in PD. The pathogenic role of HMGB1 is multifaceted: it is actively secreted by inflammatory cells and passively released by necrotic neurons, subsequently activating microglial TLR4 receptors to perpetuate a cycle of neuroinflammation ( 29 , 30 ). This chronic inflammatory state is a cornerstone of PD progression. Furthermore, the significant positive correlation we found between LnHMGB1 and the Hoehn & Yahr stage (r = 0.424, p < 0.01) provides crucial clinical relevance. It suggests that HMGB1 is not merely present but is dynamically involved in disease advancement, making it a strong candidate biomarker for staging and monitoring progression ( 31 ). The interaction of HMGB1 with α-synuclein, enhancing its oligomerization and toxicity, further cements its position within the core pathological cascade of PD ( 32 ). Parallel to HMGB1, the marked elevation of serum DKK1 offers a direct link to the dysregulation of the Wnt/β-catenin signaling pathway in PD. DKK1, a canonical Wnt antagonist, is critically involved in synaptic maintenance and neuronal survival. Its upregulation in our PD cohort suggests a mechanism where impaired Wnt signaling contributes to the synaptic disassembly and eventual loss of dopaminergic neurons in the nigrostriatal pathway ( 33 , 34 ). The powerful predictive value of DKK1 in our binary logistic regression model and its outstanding diagnostic performance in the ROC analysis (AUC = 0.81) underscore its potential as a non-invasive serum biomarker. DKK1 and HMGB1 may offer improved sensitivity and specificity compared to traditional biomarkers, which often cannot distinguish PD from similar conditions like essential tremor or multisystem atrophy ( 35 ). Both biomarkers can be measured in biofluids, aligning with the need for non-invasive diagnostic methods ( 36 ). While DKK1 and HMGB1 show promise, the search for a definitive biomarker for PD remains challenging, as no single marker has yet fulfilled all validation criteria necessary for clinical use. Elevated levels of HMGB1 and its interaction with the TLR4 axis have been observed in PD patients, correlating with disease progression and treatment outcomes ( 29 ). This finding resonates with studies in other neurodegenerative conditions, such as Alzheimer's disease, where DKK1 upregulation correlates with disease severity, indicating a common pathway of Wnt disruption in neurodegeneration ( 37 ). The concomitant elevation of both an inflammatory marker (HMGB1) and a Wnt antagonist (DKK1) suggests a potential interplay between neuroinflammation and synaptic fragility, a nexus that warrants further investigation. In contrast to these clear diagnostic markers, the roles of sclerostin and RSPO1 appear more nuanced and related to symptom expression rather than disease presence. We found no significant difference in baseline levels of sclerostin or RSPO1 between PD patients and controls. Sclerostin, primarily known for its role in bone metabolism as a Wnt antagonist ( 34 , 38 ), may not be a primary driver of PD onset, which could explain its lack of diagnostic utility. However, the significant positive correlation between sclerostin levels and motor complications (MDS-UPDRS Part IV scores) implies a potential modulatory role in the neural circuits affected by long-term levodopa therapy. This suggests that while sclerostin may not initiate the disease, it could influence the development of dyskinesias and motor fluctuations, possibly through Wnt-mediated plasticity changes in the striatum. Similarly, RSPO1, a potentiator of Wnt signaling, showed no baseline elevation. However, its positive correlations with axial motor impairment (Total-FF) and motor experiences of daily living (TOTAL-m-EDL) indicate that its levels are associated with the degree of motor disability. Furthermore, its weak but significant predictive ability for severe non-motor symptoms (AUC = 0.63) hints at a broader involvement in non-motor domains. This is plausible given the role of RSPO proteins in neurogenesis and neuronal differentiation ( 39 ). RSPO1 expression may change as a compensatory response to neuronal damage or as part of the pathological process affecting non-dopaminergic systems, making it a potential biomarker for symptom burden rather than for diagnosis. Our investigation into electrolytes revealed a complex picture. The negative correlation of magnesium with motor disability (TOTAL-m-EDL) suggests a protective role, consistent with magnesium's function as a natural NMDA receptor antagonist and its importance in mitochondrial health ( 40 ). Conversely, the positive correlation of total calcium with axial motor performance (Total-FF) aligns with studies implicating calcium dysregulation in selective neuronal vulnerability, particularly in mitochondria-rich dopaminergic neurons ( 41 , 42 ). The failure of their ratios and other parameters like albumin to show significant predictive value for symptom severity suggests that their influence is subtle and likely contingent on a multitude of other factors. Mitochondria serve as critical calcium buffers; their dysfunction leads to impaired calcium uptake and release, exacerbating calcium dysregulation in dopaminergic neurons ( 43 ). This initial state of low cytosolic calcium can deregulate signaling pathways, contributing to neuronal vulnerability ( 44 ). Calcium ion flows, which can modulate neurotransmitter release, muscle contraction, hormone secretion, and gene expression, have been found to play an essential role in the pathogenesis of PD ( 45 ). The calcium channel has been considered to have great potential as a drug target for neuroprotective therapy in PD ( 46 ). Particularly, emerging epidemiological research has evaluated the correlation between serum calcium contents and PD, and calcium dysregulation has been found in PD ( 39 ). At present, it remains unclear whether there is a causal relationship between serum calcium content and PD. Serum albumin is known for its antioxidant, anti-inflammatory, and neuroprotective properties ( 47 ). Reduced albumin levels have been observed in PD patients and are associated with disease progression, poor nutritional status, and cognitive decline ( 48 ). A critical, and perhaps the most insightful, finding of our study is the dissociation between diagnostic and progression biomarkers. While DKK1 and HMGB1 were excellent at distinguishing PD patients from healthy controls, neither could they nor any other biomarker could reliably predict severe motor symptoms. This highlights a fundamental challenge in PD biomarker research: the biological processes that initiate or define the disease may be distinct from those that drive its symptomatic progression. The complexity of non-motor symptoms, which involve diverse neuroanatomical substrates and neurotransmitter systems, makes it particularly unlikely that a single circulating biomarker like RSPO1 could capture their full severity ( 49 , 50 ). This underscores the necessity for future research to move beyond case-control designs and focus on large, longitudinal cohorts to identify biomarkers that track with clinical decline over time ( 51 ). --- Conclusion In conclusion, our results paint a detailed picture of biomarker utility in PD. We identify DKK1 and HMGB1 as robust diagnostic biomarkers linked to core pathogenic mechanisms of Wnt dysregulation and neuroinflammation. Simultaneously, we demonstrate that sclerostin and RSPO1, while not diagnostically useful, correlate with specific symptomatic aspects of the disease, suggesting their role in disease modulation. The inability of any single biomarker to predict symptom severity reinforces the notion that PD is a multisystem disorder requiring a panel of biomarkers for comprehensive profiling. Future work should aim to validate these findings in longitudinal settings and explore the therapeutic potential of modulating these key pathways. Declarations Funding This research received no external funding. Conflicts of Interest The authors declare no conflict of interest. Ethics approval and consent to participate The study was approved by the Institutional Ethics Committee of the University of Kufa (MEC-110/2025). Written informed consent was obtained from all participants. The study complied with the Declaration of Helsinki. Clinical trial registration: Clinical trial number: not applicable. Author Contribution Author ContributionTHA: Project administration, resources, methodology, writing, review, visualization, validation, investigation, data curation, and funding acquisition. HKA: Methodology, writing the original draft, writing review and editing, visualization, software, resources, investigation, supervision, and conceptualization. Acknowledgement AcknowledgmentsThe authors would like to express their gratitude to Dr. Mohsen Mohammed Al-Najm, a faculty member at the College of Medicine, Jaber Ibn Hayyan University, and consultant at the Al-Furat Al-Awsat Center for Neurosciences, and Dr. Hussein Abdulkarim Al-Barzanji, a consultant at Al-Najaf Al-Ashraf Teaching Hospital. Both are specialists in brain and nervous system disorders (Neurology) for their help in the diagnosis and severity estimation for the subjects of the study. References Bloem BR, Okun MS, Klein C (2021) Parkinson's disease. 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Neurology 84(24):2392–2393 Khoo SKJAMS (2015) Biofluid-based Biomarkers for Parkinson’s Disease: A New Paradigm. 2(4):371–373 Tay L, Leung B, Yeo A, Chan M, Lim WS (2019) Elevations in Serum Dickkopf-1 and disease progression in community-dwelling older adults with mild cognitive impairment and mild-to-moderate Alzheimer’s disease. Front Aging Neurosci 11:278 Saiki S (2014) The association of Parkinson's disease pathogenesis with inflammation. Rinsho Shinkeigaku = Clin Neurol 54(12):1125–1127 Liu J, Zhou X, Zhang L, Zhang Q, Liu C, Luo WJZ (2016) Correlation analysis of serum calcium level and cognition in the patients with Parkinson's disease. 96(41):3284–3288 Novosadova EV, Nenasheva VV, Makarova IV, Dolotov OV, Inozemtseva LS, Arsenyeva EL et al (2020) Parkinson's Disease-Associated Changes in the Expression of Neurotrophic Factors and their Receptors upon Neuronal Differentiation of Human Induced Pluripotent Stem Cells. J Mol neuroscience: MN 70(4):514–521 Drake MT, Khosla SJB (2017) Hormonal and systemic regulation of sclerostin. 96:8–17 Basir H, Altunoren O, Erken E, Kilinc M, Sarisik FN, Isiktas S et al (2019) Relationship Between Osteoporosis and Serum Sclerostin Levels in Kidney Transplant Recipients Hees JT (2019) Harbauer ABJAJoBS, Research. Calcium dysregulation and mitochondrial dysfunction form a vicious cycle in Parkinson’s disease. ;5(3) Betzer C, Jensen PHJF (2018) Reduced cytosolic calcium as an early decisive cellular state in Parkinson’s disease and synucleinopathies. 12:819 Zaichick SV, McGrath KM, Caraveo G (2017) The role of Ca(2+) signaling in Parkinson's disease. Dis Model Mech 10(5):519–535 Liss B, Striessnig, JJArop (2019) toxicology. The potential of L-type calcium channels as a drug target for neuroprotective therapy in Parkinson's disease. ;59(1):263 – 89 Sun S, Wen Y, Li Y (2022) Serum albumin, cognitive function, motor impairment, and survival prognosis in Parkinson disease. Med (Baltim) 101(37):e30324 Cui Y, Li C, Ke B, Xiao Y, Wang S, Jiang Q et al (2024) Protective role of serum albumin in dementia: a prospective study from United Kingdom biobank. 15:1458184 Todorova A, Jenner P, Chaudhuri KR (2014) Non-motor Parkinson's: integral to motor Parkinson's, yet often neglected. Pract Neurol 14(5):310–322 Jellinger KAJJont (2015) Neuropathobiology of non-motor symptoms in Parkinson disease. 122(10):1429–1440 Janssen Daalen JM, van den Bergh R, Prins EM, Moghadam MSC, van den Heuvel R, Veen J et al (2024) Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art. 7(1):186 Tables Table 1. Demographic and clinical parameters in patients with PD and healthy controls (HC). Parameter Controls PD Patients F df p Age Yrs. 61.311±5.351 63.722±13.154 1.392 1/133 0.240 Sex (female/male) 19/26 40/50 0.061 1 0.806 BMI kg/m 2 27.019±2.27 26.797±4.131 0.113 1/133 0.737 TUD No/Yes 31/14 72/18 2.046 1 0.152 Exercise No/Yes 32/13 70/20 0.722 1 0.369 Employment No/Yes 12/33 16/74 1.438 1 0.023 Residency Rural/Urban 12/33 16/74 1.438 1 0.023 Family history No/Yes 44/1 61/29 15.921 1 <0.001 Age of onset Yrs - 58.811±13.164 - - - Total-FF 11.467±2.634 40.289±7.312 - - - TOTAL-m-EDL - 29.189±7.55 - - - TOTAL-PARTIII - 54.267±11.38 - - - TOTAL-Part IV - 16.767±4.105 - - - Hoehn & Yahr stage - 2.772±0.868 - - - TOTAL-nM-EDL - 29.111±7.102 - - - Levodopa No/Yes - 27/63 - - - Kemadrin No/Yes - 65/25 - - - Sinemet No/Yes - 67/23 - - - Table 2. Results of marginal means of the serum level of neuronal damage biomarkers in healthy controls (HC) and PD patients Parameter Control Patients F p HMGB1 pg/ml 2314.895(1557.608-3604.119) 3963.256(2580.437-5063.235) MWUT <0.001 RSPO1 pm/ml 166.852(121.632-293.982) 208.536(135.844-287.613) MWUT 0.371 DKK1 ng/ml 14.299(9.678-19.458) 19.61(16.745-29.653) MWUT <0.001 Sclerostin ng/ml 8.199(5.85-21.273) 7.678(5.58-15.204) MWUT 0.204 Albumin g/dl 42.975±5.936 44.092±4.218 1.590 0.210 T.Mg mM 1.077±0.366 1±0.269 1.915 0.169 Ionized Mg mM 0.75±0.242 0.699±0.177 1.915 0.169 T.Ca mM 2.338±0.14 2.265±0.087 13.752 <0.001 Ionized Ca mM 1.221±0.041 1.2±0.024 13.971 <0.001 T.Ca/Mg 2.493±1.047 2.423±0.626 0.236 0.628 Ionized Ca/Mg 1.837±0.701 1.822±0.44 0.023 0.881 Table 3 . Partial correlation analysis between the neuropsychiatric and clinical scores and the measured biomarkers in PD patients after controlling for cofounders (age, age of onset, BMI, duration of disease, employment, exercise, residency, sex, and smoking). Parameters Total-FF TOTAL-m-EDL TOTAL-PartIII TOTAL-Part IV H & Y Stage TOTAL-nM-EDL LnHMGB1 0.153(0.172) 0.047(0.676) -0.037(0.745) 0.022(0.844) 0.424(<0.001) 0.127(0.260) LnRSPO1 0.285(0.010) -0.127(0.259) -0.004(0.971) 0.046(0.685) 0.176(0.116) 0.286(0.010) LnDKK1 0.025(0.828) -0.069(0.543) 0.112(0.321) 0.174(0.120) 0.163(0.145) 0.070(0.536) LnSclerostin -0.003(0.979) 0.113(0.315) 0.243(0.029) 0.229(0.033) -0.010(0.930) -0.065(0.565) Albumin 0.198(0.077) 0.115(0.306) 0.129(0.253) 0.174(0.120) 0.028(0.803) -0.159(0.158) T.& I.Mg 0.067(0.549) -0.117(0.296) 0.021(0.856) 0.060(0.595) 0.023(0.836) -0.276(0.013) T.Ca mg/dl -0.156(0.163) -0.031(0.782) -0.046(0.684) -0.042(0.711) 0.036(0.750) -0.066(0.558) I. Ca -0.223(0.036) -0.063(0.574) -0.081(0.473) -0.09(0.422) 0.025(0.826) -0.148(0.188) T.Ca/Mg -0.102(0.363) 0.073(0.517) -0.067(0.551) -0.107(0.340) -0.069(0.538) 0.120(0.287) I.Ca/Mg -0.097(0.391) 0.078(0.490) -0.062(0.585) -0.106(0.345) -0.071(0.526) 0.119(0.289) Table 4. Results of binary logistic regression analyses. First, comparing PD patients with healthy controls, and Second, comparing patients with severe nonmotor symptoms with those with moderate nonmotor symptoms. Third, with severe motor versus moderate motor symptoms as dependent variables and biomarkers as explanatory variables. Target Explanatory variables B(SE) Wald P OR (95% CI) Sensitivity, Specificity PD DKK1 ng/ml 0.247(0.058) 18.297 <0.001 1.280(1.143-1.434) 74%, 90% HMGB1 pg/ml 0.001(0.001) 9.042 0.003 1.000(1.000-1.001 RSPO1 pm/ml 0.001(0.002) 0.417 0.518 1.001(0.997-1.005) Sclerostin ng/ml -0.004(0.024) 0.024 0.877 0.996(0.950-1.045) χ 2 =63.564, df=4, Nagelkerke R 2 =0.522, p<0.001 Severe Nonmotor χ 2 =4.642, df=4, Nagelkerke R 2 =0.069, p=0.326 Sever Motor χ 2 =4.140, df=4, Nagelkerke R 2 =0.061, p=0.387 Table 5. Receiver operating characteristic-area under curve (AUC) analysis of the biomarkers in the prediction of severe Motor in PD patients. CI: Confidence interval. Discrimination Parameters Cut-off Sensitivity % Specificity % Youden's J statistic AUC(CI 95%) p PD vs. Controls HMGB1 pg/ml 3009.30 68.9 68.9 0.378 0.72(0.62-0.82) <0.001 RSPO1 pm/ml 238.09 58.9 60.0 0.189 0.64(0.54-0.74) 0.008 DKK1 ng/ml 17.84 66.7 66.7 0.334 0.81(0.74-0.89) <0.001 T.Ca mg/dl 2.30 64.4 65.0 0.294 0.71(0.60-0.81) <0.001 Ionized Ca 1.2 64.4 64.6 0.290 0.70(0.61-0.82) <0.001 Sclerostin, Albumin, T. & I.Mg, T.Ca/Mg, and Ionized Ca/Mg - - - - 0.05 Severe nonmotor RSPO1 pm/ml 254.05 61.4 60.6 0.220 0.63(0.07) 0.036 DKK1, HMGB1, Sclerostin, Albumin, T. & I.Mg, T.& I.Ca, T.Ca/Mg, and Ionized Ca/Mg - - - - 0.05 Severe motor All biomarkers - - - - 0.05 Additional Declarations No competing interests reported. Supplementary Files GA.png Graphical abstract Title: Dysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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08:21:14","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109002,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7919099/v1/1a48136d038f54f5606c8334.html"},{"id":95691226,"identity":"25fb121d-772e-4fee-aeec-c64ab17fc4fe","added_by":"auto","created_at":"2025-11-12 02:18:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curves of the biomarkers in the prediction of PD patients against healthy controls.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7919099/v1/1b11706881635ab16af1a670.png"},{"id":98440714,"identity":"51243c72-ca36-4ba6-9d4d-1ff7c75d0141","added_by":"auto","created_at":"2025-12-17 17:04:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7919099/v1/26718b9b-176b-476e-8225-f0e99049a1f2.pdf"},{"id":95691225,"identity":"ae3b1866-380f-4358-9e9e-626124b3e6a2","added_by":"auto","created_at":"2025-11-12 02:18:29","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle\u003c/strong\u003e: \u003cstrong\u003eDysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7919099/v1/6238599632bee377b7178539.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a progressive neurodegenerative disorder that extends beyond its classic motor symptoms of tremor, rigidity, and bradykinesia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Non-motor symptoms, including cognitive impairment, often precede motor onset by years and significantly shape disease progression and quality of life (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The pathophysiological hallmarks of PD include the aggregation of alpha-synuclein into Lewy bodies and the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to striatal dopamine deficiency (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Current treatments, such as levodopa and deep-brain stimulation, offer symptomatic relief but do not delay disease progression, highlighting an urgent need for novel therapeutic targets and predictive biomarkers (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Among the less-explored pathways in PD, the Wingless/integrated-1 (Wnt)/β-catenin signaling cascade has emerged as a key regulator of neurodevelopment, synaptic plasticity, and adult neurogenesis (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The Wnt/β-catenin signaling pathway, a critical regulator of embryonic development and adult tissue homeostasis, has emerged as a promising area of investigation in neurodegeneration (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The Wnt/β-catenin signaling pathway is an important cellular signaling pathway involved in various biological processes such as apoptosis, proliferation, fibrogenesis, homeostasis, differentiation, growth, and repair (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In the central nervous system, this pathway supports neurogenesis, synaptic plasticity, and neuronal survival (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Importantly, its activation can protect dopaminergic neurons by improving mitochondrial function and reducing oxidative stress (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Studies have shown that the Wnt/β-catenin pathway can regulate mitochondrial function in PD, maintaining mitochondrial membrane potential, altering mitochondrial morphology, and reducing the release of reactive oxygen species (ROS), thus protecting dopaminergic neurons (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Endogenous agonists and antagonists precisely modulate the pathway. Dickkopf-1 (DKK1) is a potent secreted antagonist that inhibits Wnt/β-catenin signaling by binding to the LRP5/6 co-receptor (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and its upregulation is linked to neuronal death guidance (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Conversely, R-spondin 1 (RSPO1) acts as an agonist that can potentiate Wnt signaling and attenuate DKK1-mediated inhibition signaling (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConcurrently, disruptions in cellular homeostasis are implicated in PD pathogenesis. The alarmin High Mobility Group Box 1 (HMGB1), when released extracellularly, acts as a potent mediator of neuroinflammation, and its levels are elevated in PD patients (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Upon extracellular release, HMGB1 orchestrates inflammatory cascades, immunological responses, and drives neurodegeneration and cardiovascular complications (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The serum HMGB1 level of PD patients was higher than that of the controls (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). HMGB1 can bind to alpha-synuclein, potentially catalyzing the neurodegenerative process (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, calcium homeostasis is critical for substantia nigra dopaminergic neurons, regulating functions from excitability to energy production (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Defects in calcium processing are known to play an essential role in aging and neurodegeneration, and alpha-synuclein aggregation can directly disrupt calcium homeostasis, creating a vicious cycle of toxicity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Calcium ions are regulated by various pathways (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). \u003cem\u003eα\u003c/em\u003e-synaptic nucleoprotein aggregation, an important pathologic feature of PD, leads to disruption of calcium homeostasis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). While the roles of Wnt signaling, neuroinflammation, and ionic imbalance have been studied in isolation, their interplay in predicting PD symptomatology remains largely unexplored. Therefore, this study aims to investigate the potential of a panel of circulating biomarkers, including key regulators of the Wnt/β-catenin pathway (DKK1, RSPO1), and key mediators of neurodegeneration like HMGB1 and divalent cations (calcium and magnesium) in the context of PD symptomatology, as predictors for the development and severity of motor and non-motor symptoms in PD.\u003c/p\u003e"},{"header":"Subjects and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe present case-control study examined a group of ninety patients diagnosed with PD and a control group of forty-five healthy individuals. The specimens were collected from Al-Sadr Medical City, Al-Najaf Teaching Hospital, and Al-Furat Al-Awsat Center for Neurosciences in Najaf city, Iraq, during the period from February to May 2025. The assessment of patients was carried out by taking a complete medical history and clinical examination. The diagnosis of PD was carried out using the UK Parkinson's Disease Society Brain Bank Clinical Diagnostic Criteria (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). All patients had bradykinesia along with a minimum of one other cardinal symptom (resting tremor, stiffness, or postural instability) and showed no characteristics indicative of an alternative parkinsonian syndrome (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). For the motor assessment, we used Part III (Motor Examination) of the MDS-UPDRS to rate the severity of MS (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). To maintain consistency, patients were assessed in the 'OFF' drug state, after a minimum 12-hour cessation of all dopaminergic medicines. A combination of tools was used to describe the severity and burden of non-MS. The NMS scale was used for a thorough assessment (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The Hoehn and Yahr staging method was also used to rate how bad the epidemic was across the world (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The same qualified neurologist gave all of the clinician-rated scales. The neurologist explored the presence of any systemic disease that may affect the studied parameters, especially liver disease and kidney disease, which were excluded from the study. In order to rule out the possibility of any overt systemic inflammation, the serum C-reactive protein (CRP) levels in all of the samples came back negative, coming in at less than 6 mg/l (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The control and patient participants were required to give written consent before participating in the study. They were provided with detailed information beforehand. The study was granted approval by the Institutional Ethics Committee of the University of Kufa (MEC-110/2025). The study followed ethical and privacy laws both in Iraq and internationally. It complied with various international guidelines and declarations, such as the World Medical Association's Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical measurements\u003c/h3\u003e\n\u003cp\u003eAn experienced neurologist conducted a semi-structured interview to evaluate and gather sociodemographic and clinical information from control subjects and patients. An expert in neurology assessed the extent of the motor and MS associated with PD by employing the Movement Disorders Society Revision of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) as outlined previously (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The MDS-UPDRS is divided into four parts: Part I: non-motor Experiences of Daily Living (nM-EDL), Part II: motor Experiences of Daily Living (m-EDL), Part III: motor examination, and Part IV: motor complications (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Neurologists conducted the MDS-UPDRS assessment on patients, utilizing the ratings from the four domains for statistical analysis. The severity of CFS and fibromyalgia was assessed by a senior psychiatrist using the Fibro-Fatigue scale (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssays\u003c/h3\u003e\n\u003cp\u003eA volume of 5 milliliters of fasting blood samples was collected at approximately 9:00 a.m. After complete clotting, the blood samples were centrifuged at 1,200 \u0026times; g for five minutes at room temperature. Subsequently, the serum was carefully divided and distributed among three Eppendorf tubes. Excluded from the study were samples that had undergone hemolysis. The tubes were subsequently frozen at -80\u0026deg;C and remained in this state until they were thawed for the assays. We utilized sandwich ELISA techniques to quantify the levels of HMGB1, RSPO1, DKK1, and Sclerostin using ELISA kits provided by Wuhan USCN Business Co., Ltd. (China). Albumin, T.Mg, Ionized Mg, T.Ca, Ionized Ca, T.Ca/Mg and Ionized Ca/Mg were measured in serum using colorimetric kits from Spectrum\u0026reg; in Cairo, Egypt. The coefficient of variation (CV) for all ELISA kits was less than 10.0%. We employed sample dilutions for samples that contained analytes with elevated concentrations by 1:5 with the sample diluent provided with the kit.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eBased on the statistical distribution, the analysis's findings divided the variables into two groups: normally distributed and nonparametric variables identified by the results of the Kolmogorov-Smirnov test. A normal distribution of the results was given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The nonparametric variables' values are shown as medians and 25%\u0026ndash;75% percentiles. The comparison between the patient and control groups was done using the Mann-Whitney U-test for non-normally distributed variables, and one-way analysis of variance was used to compare scale variables across groups distributed normally. The contingency tables (χ\u003csup\u003e2\u003c/sup\u003e tests) assessed the associations between categorical variables. Pearson's product-moment correlation was used to analyze the relationships between scale variables and the biomarkers after transforming non-normally distributed variables into the Ln transformation. Using multivariate general linear model (GLM) analysis (followed by tests of between-subject effects), the associations between categories and biomarkers were investigated, accounting for confounding variables including age, sex, and body mass index (BMI). From the multivariate GLM, we computed the estimated marginal mean of variables after controlling for all covariates. We performed a binary logistic regression analysis using the diagnosis of severe versus moderate MS and NMS in the PD group, with biomarkers serving as explanatory variables. The odds ratio with 95% confidence intervals was calculated, along with the predictive accuracy, sensitivity, and specificity. The latter was utilized to estimate the effect size of the model. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic efficacy of the identified biomarkers for the diagnosis of severe fatigue in PE patients. The concentration cut-off values, determined by the area under the curve (AUC), provide optimal sensitivity and specificity. Confidence intervals were also determined to evaluate the precision of the calculated AUC; a narrower interval signifies a more certain conclusion. A higher outcome for Youden's J statistic indicates that the biomarker increases with diagnosis. We used the concentration that aligned with the maximum Youden's J statistic as the cut-off values. In this study, statistical significance was established at a p-value of 0.05 using two-tailed tests. IBM SPSS 26 for Windows was utilized to analyze the data. G*Power 3.1.9.7 showed that the a priori estimated sample size was 134, given a power of 0.90, alpha\u0026thinsp;=\u0026thinsp;0.05, and effect size\u0026thinsp;=\u0026thinsp;0.28.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic and clinical data\u003c/h2\u003e\u003cp\u003eThe results of sociodemographic and clinical data are presented in Table\u0026nbsp;1 In the current study, comparison between PD patients and healthy controls revealed no statistically significant differences in age, BMI, sex distribution, residency, exercise, TUD, and marital status. PD patients had significantly lower education levels, possibly indicating reduced health awareness and less cognitive stimulation. More PD patients were unemployed or retired, likely due to early symptoms affecting work ability. Family history is significantly higher in PD patients than in controls. The PD group exhibited a moderate level of illness severity, as shown by a mean Hoehn \u0026amp; Yahr stage of 2.772\u0026thinsp;\u0026plusmn;\u0026thinsp;0.868. The UPDRS measured MS and gave the following scores: Part III (motor examination) was 54.267\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38, and Part IV (motor complications)\u0026thinsp;=\u0026thinsp;16.767\u0026thinsp;\u0026plusmn;\u0026thinsp;4.105. PD patients were taking medication: 70% (63/90) were using Levodopa, 27.8% (25/90) were taking Kemadrin (procyclidine), and 25.6% (23/90) were taking Sinemet (a mix of carbidopa and levodopa).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferences in the biomarkers between PD patient groups and controls\u003c/h3\u003e\n\u003cp\u003eThe results in Table\u0026nbsp;2 demonstrated significant differences in serum neuronal damage biomarkers between HC and PD patients. Notably, DKK1 and HMGB1 levels were markedly elevated in the PD group compared with HC, with highly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The findings revealed no significant differences in sclerostin (p\u0026thinsp;=\u0026thinsp;0.204) and RSPO1 (p\u0026thinsp;=\u0026thinsp;0.371) levels among the two groups.\u003c/p\u003e\n\u003ch3\u003eCorrelation matrix between the neuropsychiatric and clinical scores and the measured biomarkers\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;3 displays the partial correlation matrix between serum biomarkers and clinical scores among Parkinson\u0026rsquo;s disease (PD) patients. LnHMGB1 showed a positive correlation with Hoehn \u0026amp; Yahr stage (r\u0026thinsp;=\u0026thinsp;0.424, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating its potential role as an inflammatory marker linked to disease progression. LnRSPO1 was positively correlated with both Total-FF (r\u0026thinsp;=\u0026thinsp;0.285, p\u0026thinsp;=\u0026thinsp;0.010) and TOTAL-m-EDL (r\u0026thinsp;=\u0026thinsp;0.286, p\u0026thinsp;=\u0026thinsp;0.010) performance. Sclerostin was positively associated with TOTAL Part IV scores (r\u0026thinsp;=\u0026thinsp;0.229, p\u0026thinsp;=\u0026thinsp;0.033),, total magnesium demonstrated a negative correlation with TOTAL-m-EDL (r=\u0026ndash;0.276, p\u0026thinsp;=\u0026thinsp;0.013), whereas total calcium was positively correlated with Total-FF (r\u0026thinsp;=\u0026thinsp;0.223, p\u0026thinsp;=\u0026thinsp;0.036), In contrast, other biomarkers, including LnDKK1, albumin, ionized calcium, and calcium-to-magnesium ratios (T.Ca/Mg and I.Ca/Mg)\u0026mdash;did not show significant correlations with any of the clinical parameters assessed.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBinary logistic regression analyses\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;4 presents the results of the binary logistic regression analysis used to evaluate the predictive value of circulating biomarkers in distinguishing Parkinson\u0026rsquo;s disease (PD) patients from healthy controls. Among the analyzed markers, DKK1 and HMGB1 emerged as significant predictors. DKK1 showed a strong positive association with PD diagnosis (B\u0026thinsp;=\u0026thinsp;0.247, SE\u0026thinsp;=\u0026thinsp;0.058, Wald\u0026thinsp;=\u0026thinsp;18.297, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an odds ratio (OR) of 1.280 (95% CI: 1.143\u0026ndash;1.434), indicating that elevated serum DKK1 levels may reflect underlying neurodegenerative processes linked to Wnt signaling dysregulation. HMGB1 was also significantly associated with PD (B\u0026thinsp;=\u0026thinsp;0.001, SE\u0026thinsp;=\u0026thinsp;0.001, Wald\u0026thinsp;=\u0026thinsp;9.042, p\u0026thinsp;=\u0026thinsp;0.003), albeit with an OR of 1.000 (95% CI: 1.000\u0026ndash;1.001), suggesting that even minimal changes in HMGB1 levels might contribute to disease risk through inflammatory mechanisms. The overall model demonstrated strong diagnostic performance, with 74% sensitivity and 90% specificity, and was statistically significant (χ\u0026sup2; = 63.564, df\u0026thinsp;=\u0026thinsp;4, R\u0026sup2; = 0.522, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).In contrast, RSPO1 (p\u0026thinsp;=\u0026thinsp;0.518) and sclerostin (p\u0026thinsp;=\u0026thinsp;0.877) did not show significant predictive value for PD. Furthermore, when evaluating the ability of biomarkers to distinguish between severe and moderate non-motor symptoms, no significant predictors were identified. Similarly, the model assessing motor symptom severity showed no significant findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of PD vs. healthy controls\u003c/h2\u003e\u003cp\u003eThe results of receiver operating characteristic-area under the curve (AUC) analysis of the biomarkers for predicting severe motor and non-motor in PD patients are presented in Table\u0026nbsp;5 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the biomarkers, DKK1 demonstrated the highest predictive performance with an AUC of 0.81 (95% CI: 0.74\u0026ndash;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by HMGB1 with an AUC of 0.72 (95% CI: 0.62\u0026ndash;0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results indicate that higher circulating levels of DKK1 and HMGB1 are associated with PD, supporting their potential utility as diagnostic biomarkers. RSPO1 and calcium parameters (total and ionized) showed moderate predictive ability, whereas other markers such as Sclerostin, Albumin, Mg, and Ca/Mg ratios were not useful (AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.50, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrediction of severe non-motor symptom\u003c/b\u003es\u003c/p\u003e\u003cp\u003eOnly RSPO1 showed weak predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.63, p\u0026thinsp;=\u0026thinsp;0.036), while all other biomarkers had AUC values below 0.50, indicating no significant discrimination. This suggests that the examined biomarkers are largely insufficient for predicting the severity of non-motor symptoms in PD.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of severe motor symptoms\u003c/h2\u003e\u003cp\u003eAll biomarkers demonstrated AUC values below 0.50 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating no predictive value for distinguishing severe versus moderate motor symptoms.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides compelling evidence for the involvement of specific Wnt signaling modulators and inflammatory mediators in PD, differentiating between their utility as diagnostic biomarkers and their correlation with symptom severity. The central finding is the highly significant elevation of serum DKK1 and HMGB1 in PD patients compared to healthy controls, positioning them as key players in the disease's pathophysiology. The increase in serum levels of DKK1 and HMGB1 in PD is primarily attributed to neuroinflammatory processes and the pathological aggregation of proteins. HMGB1 is actively secreted by inflammatory cells and passively released by necrotic cells, playing a significant role in neuroinflammation, autophagy modulation, and apoptosis regulation in PD. The pathogenic role of HMGB1 is multifaceted: it is actively secreted by inflammatory cells and passively released by necrotic neurons, subsequently activating microglial TLR4 receptors to perpetuate a cycle of neuroinflammation (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This chronic inflammatory state is a cornerstone of PD progression. Furthermore, the significant positive correlation we found between LnHMGB1 and the Hoehn \u0026amp; Yahr stage (r\u0026thinsp;=\u0026thinsp;0.424, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) provides crucial clinical relevance. It suggests that HMGB1 is not merely present but is dynamically involved in disease advancement, making it a strong candidate biomarker for staging and monitoring progression (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The interaction of HMGB1 with α-synuclein, enhancing its oligomerization and toxicity, further cements its position within the core pathological cascade of PD (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParallel to HMGB1, the marked elevation of serum DKK1 offers a direct link to the dysregulation of the Wnt/β-catenin signaling pathway in PD. DKK1, a canonical Wnt antagonist, is critically involved in synaptic maintenance and neuronal survival. Its upregulation in our PD cohort suggests a mechanism where impaired Wnt signaling contributes to the synaptic disassembly and eventual loss of dopaminergic neurons in the nigrostriatal pathway (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The powerful predictive value of DKK1 in our binary logistic regression model and its outstanding diagnostic performance in the ROC analysis (AUC\u0026thinsp;=\u0026thinsp;0.81) underscore its potential as a non-invasive serum biomarker. DKK1 and HMGB1 may offer improved sensitivity and specificity compared to traditional biomarkers, which often cannot distinguish PD from similar conditions like essential tremor or multisystem atrophy (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Both biomarkers can be measured in biofluids, aligning with the need for non-invasive diagnostic methods (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). While DKK1 and HMGB1 show promise, the search for a definitive biomarker for PD remains challenging, as no single marker has yet fulfilled all validation criteria necessary for clinical use. Elevated levels of HMGB1 and its interaction with the TLR4 axis have been observed in PD patients, correlating with disease progression and treatment outcomes (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This finding resonates with studies in other neurodegenerative conditions, such as Alzheimer's disease, where DKK1 upregulation correlates with disease severity, indicating a common pathway of Wnt disruption in neurodegeneration (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The concomitant elevation of both an inflammatory marker (HMGB1) and a Wnt antagonist (DKK1) suggests a potential interplay between neuroinflammation and synaptic fragility, a nexus that warrants further investigation.\u003c/p\u003e\u003cp\u003eIn contrast to these clear diagnostic markers, the roles of sclerostin and RSPO1 appear more nuanced and related to symptom expression rather than disease presence. We found no significant difference in baseline levels of sclerostin or RSPO1 between PD patients and controls. Sclerostin, primarily known for its role in bone metabolism as a Wnt antagonist (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), may not be a primary driver of PD onset, which could explain its lack of diagnostic utility. However, the significant positive correlation between sclerostin levels and motor complications (MDS-UPDRS Part IV scores) implies a potential modulatory role in the neural circuits affected by long-term levodopa therapy. This suggests that while sclerostin may not initiate the disease, it could influence the development of dyskinesias and motor fluctuations, possibly through Wnt-mediated plasticity changes in the striatum.\u003c/p\u003e\u003cp\u003eSimilarly, RSPO1, a potentiator of Wnt signaling, showed no baseline elevation. However, its positive correlations with axial motor impairment (Total-FF) and motor experiences of daily living (TOTAL-m-EDL) indicate that its levels are associated with the degree of motor disability. Furthermore, its weak but significant predictive ability for severe non-motor symptoms (AUC\u0026thinsp;=\u0026thinsp;0.63) hints at a broader involvement in non-motor domains. This is plausible given the role of RSPO proteins in neurogenesis and neuronal differentiation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). RSPO1 expression may change as a compensatory response to neuronal damage or as part of the pathological process affecting non-dopaminergic systems, making it a potential biomarker for symptom burden rather than for diagnosis.\u003c/p\u003e\u003cp\u003eOur investigation into electrolytes revealed a complex picture. The negative correlation of magnesium with motor disability (TOTAL-m-EDL) suggests a protective role, consistent with magnesium's function as a natural NMDA receptor antagonist and its importance in mitochondrial health (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Conversely, the positive correlation of total calcium with axial motor performance (Total-FF) aligns with studies implicating calcium dysregulation in selective neuronal vulnerability, particularly in mitochondria-rich dopaminergic neurons (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The failure of their ratios and other parameters like albumin to show significant predictive value for symptom severity suggests that their influence is subtle and likely contingent on a multitude of other factors. Mitochondria serve as critical calcium buffers; their dysfunction leads to impaired calcium uptake and release, exacerbating calcium dysregulation in dopaminergic neurons (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This initial state of low cytosolic calcium can deregulate signaling pathways, contributing to neuronal vulnerability (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Calcium ion flows, which can modulate neurotransmitter release, muscle contraction, hormone secretion, and gene expression, have been found to play an essential role in the pathogenesis of PD (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The calcium channel has been considered to have great potential as a drug target for neuroprotective therapy in PD (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Particularly, emerging epidemiological research has evaluated the correlation between serum calcium contents and PD, and calcium dysregulation has been found in PD (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). At present, it remains unclear whether there is a causal relationship between serum calcium content and PD. Serum albumin is known for its antioxidant, anti-inflammatory, and neuroprotective properties (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Reduced albumin levels have been observed in PD patients and are associated with disease progression, poor nutritional status, and cognitive decline (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA critical, and perhaps the most insightful, finding of our study is the dissociation between diagnostic and progression biomarkers. While DKK1 and HMGB1 were excellent at distinguishing PD patients from healthy controls, neither could they nor any other biomarker could reliably predict severe motor symptoms. This highlights a fundamental challenge in PD biomarker research: the biological processes that initiate or define the disease may be distinct from those that drive its symptomatic progression. The complexity of non-motor symptoms, which involve diverse neuroanatomical substrates and neurotransmitter systems, makes it particularly unlikely that a single circulating biomarker like RSPO1 could capture their full severity (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). This underscores the necessity for future research to move beyond case-control designs and focus on large, longitudinal cohorts to identify biomarkers that track with clinical decline over time (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e---\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our results paint a detailed picture of biomarker utility in PD. We identify DKK1 and HMGB1 as robust diagnostic biomarkers linked to core pathogenic mechanisms of Wnt dysregulation and neuroinflammation. Simultaneously, we demonstrate that sclerostin and RSPO1, while not diagnostically useful, correlate with specific symptomatic aspects of the disease, suggesting their role in disease modulation. The inability of any single biomarker to predict symptom severity reinforces the notion that PD is a multisystem disorder requiring a panel of biomarkers for comprehensive profiling. Future work should aim to validate these findings in longitudinal settings and explore the therapeutic potential of modulating these key pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003cp\u003eConflicts of Interest\u003c/p\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\u003cp\u003e The study was approved by the Institutional Ethics Committee of the University of Kufa (MEC-110/2025). Written informed consent was obtained from all participants. The study complied with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eClinical trial registration: Clinical trial number: not applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionTHA: Project administration, resources, methodology, writing, review, visualization, validation, investigation, data curation, and funding acquisition. HKA: Methodology, writing the original draft, writing review and editing, visualization, software, resources, investigation, supervision, and conceptualization.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgmentsThe authors would like to express their gratitude to Dr. Mohsen Mohammed Al-Najm, a faculty member at the College of Medicine, Jaber Ibn Hayyan University, and consultant at the Al-Furat Al-Awsat Center for Neurosciences, and Dr. Hussein Abdulkarim Al-Barzanji, a consultant at Al-Najaf Al-Ashraf Teaching Hospital. Both are specialists in brain and nervous system disorders (Neurology) for their help in the diagnosis and severity estimation for the subjects of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBloem BR, Okun MS, Klein C (2021) Parkinson's disease. Lancet (London England) 397(10291):2284\u0026ndash;2303\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonzalez-Latapi P, Bayram E, Litvan I, Marras CJBS (2021) Cognitive impairment in Parkinson\u0026rsquo;s disease: epidemiology, clinical profile, protective and risk factors. 11(5):74\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePardo-Moreno T, Garcia-Morales V, Suleiman-Martos S, Rivas-Dominguez A, Mohamed-Mohamed H, Ramos-Rodriguez JJ et al (2023) Current Treatments and New, Tentative Therapies for Parkinson's Disease. 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Mov Disord 23(15):2129\u0026ndash;2170\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZachrisson H, Blomstrand C, Holm J, Mattsson E, Volkmann R (2002) Changes in middle cerebral artery blood flow after carotid endarterectomy as monitored by transcranial Doppler. J Vasc Surg 36(2):285\u0026ndash;290\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y, Han C, Guo L, Guan Q (2018) High expression of the HMGB1-TLR4 axis and its downstream signaling factors in patients with Parkinson's disease and the relationship of pathological staging. Brain Behav 8(4):e00948\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao H-M, Zhou H, Zhang F, Wilson BC, Kam W, Hong J-SJJN (2011) HMGB1 acts on microglia Mac1 to mediate chronic neuroinflammation that drives progressive neurodegeneration. 31(3):1081\u0026ndash;1092\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIkram FZ, Arulsamy A, Retinasamy T, Shaikh MF (2022) The role of high mobility group box 1 (HMGB1) in neurodegeneration: a systematic review. Curr Neuropharmacol 20(11):2221\u0026ndash;2245\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKo EA, Min HJ, Shin J-S (2012) Interaction of High Mobility Group Box-1 (HMGB1) with α-synuclein and its aggregation (172.28). J Immunol 188(1Supplement):172\u0026ndash;128\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePurro SA, Galli S, Salinas PC (2014) Dysfunction of Wnt signaling and synaptic disassembly in neurodegenerative diseases. J Mol Cell Biol 6(1):75\u0026ndash;80\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosi MC, Luccarini I, Grossi C, Fiorentini A, Spillantini MG, Prisco A et al (2010) Increased Dickkopf-1 expression in transgenic mouse models of neurodegenerative disease. J Neurochem 112(6):1539\u0026ndash;1551\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKeown MJ, Peavy GM (2015) Biomarkers in Parkinson disease: It's time to combine. Neurology 84(24):2392\u0026ndash;2393\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhoo SKJAMS (2015) Biofluid-based Biomarkers for Parkinson\u0026rsquo;s Disease: A New Paradigm. 2(4):371\u0026ndash;373\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTay L, Leung B, Yeo A, Chan M, Lim WS (2019) Elevations in Serum Dickkopf-1 and disease progression in community-dwelling older adults with mild cognitive impairment and mild-to-moderate Alzheimer\u0026rsquo;s disease. Front Aging Neurosci 11:278\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaiki S (2014) The association of Parkinson's disease pathogenesis with inflammation. Rinsho Shinkeigaku = Clin Neurol 54(12):1125\u0026ndash;1127\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Zhou X, Zhang L, Zhang Q, Liu C, Luo WJZ (2016) Correlation analysis of serum calcium level and cognition in the patients with Parkinson's disease. 96(41):3284\u0026ndash;3288\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNovosadova EV, Nenasheva VV, Makarova IV, Dolotov OV, Inozemtseva LS, Arsenyeva EL et al (2020) Parkinson's Disease-Associated Changes in the Expression of Neurotrophic Factors and their Receptors upon Neuronal Differentiation of Human Induced Pluripotent Stem Cells. J Mol neuroscience: MN 70(4):514\u0026ndash;521\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDrake MT, Khosla SJB (2017) Hormonal and systemic regulation of sclerostin. 96:8\u0026ndash;17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBasir H, Altunoren O, Erken E, Kilinc M, Sarisik FN, Isiktas S et al (2019) Relationship Between Osteoporosis and Serum Sclerostin Levels in Kidney Transplant Recipients\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHees JT (2019) Harbauer ABJAJoBS, Research. Calcium dysregulation and mitochondrial dysfunction form a vicious cycle in Parkinson\u0026rsquo;s disease. ;5(3)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBetzer C, Jensen PHJF (2018) Reduced cytosolic calcium as an early decisive cellular state in Parkinson\u0026rsquo;s disease and synucleinopathies. 12:819\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZaichick SV, McGrath KM, Caraveo G (2017) The role of Ca(2+) signaling in Parkinson's disease. Dis Model Mech 10(5):519\u0026ndash;535\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiss B, Striessnig, JJArop (2019) toxicology. The potential of L-type calcium channels as a drug target for neuroprotective therapy in Parkinson's disease. ;59(1):263\u0026thinsp;\u0026ndash;\u0026thinsp;89\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun S, Wen Y, Li Y (2022) Serum albumin, cognitive function, motor impairment, and survival prognosis in Parkinson disease. Med (Baltim) 101(37):e30324\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCui Y, Li C, Ke B, Xiao Y, Wang S, Jiang Q et al (2024) Protective role of serum albumin in dementia: a prospective study from United Kingdom biobank. 15:1458184\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTodorova A, Jenner P, Chaudhuri KR (2014) Non-motor Parkinson's: integral to motor Parkinson's, yet often neglected. Pract Neurol 14(5):310\u0026ndash;322\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJellinger KAJJont (2015) Neuropathobiology of non-motor symptoms in Parkinson disease. 122(10):1429\u0026ndash;1440\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanssen Daalen JM, van den Bergh R, Prins EM, Moghadam MSC, van den Heuvel R, Veen J et al (2024) Digital biomarkers for non-motor symptoms in Parkinson\u0026rsquo;s disease: the state of the art. 7(1):186\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic and clinical parameters in patients with PD and healthy controls (HC).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003ePD Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eAge Yrs.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e61.311\u0026plusmn;5.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e63.722\u0026plusmn;13.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1/133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSex (female/male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e19/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e40/50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eBMI kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e27.019\u0026plusmn;2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e26.797\u0026plusmn;4.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1/133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eTUD No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e31/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e72/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eExercise No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e32/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e70/20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eEmployment No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e12/33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e16/74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eResidency Rural/Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e12/33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e16/74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eFamily history No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e44/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e61/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e15.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003eAge of onset Yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e58.811\u0026plusmn;13.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eTotal-FF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e11.467\u0026plusmn;2.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e40.289\u0026plusmn;7.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eTOTAL-m-EDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e29.189\u0026plusmn;7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eTOTAL-PARTIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e54.267\u0026plusmn;11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eTOTAL-Part IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e16.767\u0026plusmn;4.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eHoehn \u0026amp; Yahr stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2.772\u0026plusmn;0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eTOTAL-nM-EDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e29.111\u0026plusmn;7.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eLevodopa No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e27/63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eKemadrin No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e65/25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eSinemet No/Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e67/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Results of marginal means of the serum level of neuronal damage biomarkers in healthy controls (HC) and PD patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"707\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHMGB1 pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2314.895(1557.608-3604.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e3963.256(2580.437-5063.235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRSPO1 pm/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e166.852(121.632-293.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e208.536(135.844-287.613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eDKK1 ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e14.299(9.678-19.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e19.61(16.745-29.653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eSclerostin ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e8.199(5.85-21.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e7.678(5.58-15.204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMWUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Albumin g/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e42.975\u0026plusmn;5.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e44.092\u0026plusmn;4.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;T.Mg mM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.077\u0026plusmn;0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1\u0026plusmn;0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIonized Mg mM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0.699\u0026plusmn;0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003eT.Ca mM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2.338\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2.265\u0026plusmn;0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e13.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIonized Ca mM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.221\u0026plusmn;0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.2\u0026plusmn;0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e13.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003eT.Ca/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2.493\u0026plusmn;1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e2.423\u0026plusmn;0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIonized Ca/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.837\u0026plusmn;0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 222px;\"\u003e\n \u003cp\u003e1.822\u0026plusmn;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Partial correlation analysis between the neuropsychiatric and clinical scores and the measured biomarkers in PD patients after controlling for cofounders (age, age of onset, BMI, duration of disease, employment, exercise, residency, sex, and smoking).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"776\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003eTotal-FF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eTOTAL-m-EDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003eTOTAL-PartIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTOTAL-Part IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eH \u0026amp; Y Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eTOTAL-nM-EDL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLnHMGB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.153(0.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.047(0.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.037(0.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.022(0.844)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.424(\u0026lt;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.127(0.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLnRSPO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.285(0.010)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.127(0.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.004(0.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.046(0.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.176(0.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.286(0.010)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLnDKK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.025(0.828)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.069(0.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.112(0.321)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.174(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.163(0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.070(0.536)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eLnSclerostin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e-0.003(0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.113(0.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.243(0.029)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.229(0.033)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.010(0.930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.065(0.565)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.198(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.115(0.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.129(0.253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.174(0.120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.028(0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.159(0.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eT.\u0026amp; I.Mg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.067(0.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.117(0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.021(0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.060(0.595)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.023(0.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.276(0.013)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eT.Ca mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e-0.156(0.163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.031(0.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.046(0.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.042(0.711)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.036(0.750)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.066(0.558)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eI. Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.223(0.036)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.063(0.574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.081(0.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.09(0.422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.025(0.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.148(0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eT.Ca/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e-0.102(0.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.073(0.517)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.067(0.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.107(0.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.069(0.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.120(0.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eI.Ca/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e-0.097(0.391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.078(0.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e-0.062(0.585)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.106(0.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.071(0.526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.119(0.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Results of binary logistic regression analyses. First, comparing PD patients with healthy controls, and Second, comparing patients with severe nonmotor symptoms with those with moderate nonmotor symptoms. Third, with severe motor versus moderate motor symptoms as dependent variables and biomarkers as explanatory variables.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"659\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplanatory variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity, Specificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eDKK1 ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.247(0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e18.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.280(1.143-1.434)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74%, 90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eHMGB1 pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.001(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e9.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.000(1.000-1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eRSPO1 pm/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.001(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1.001(0.997-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSclerostin ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.004(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e0.996(0.950-1.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 490px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026chi;\u003csup\u003e2\u003c/sup\u003e=63.564, df=4, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e =0.522, p\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eSevere Nonmotor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e=4.642, df=4, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e =0.069, p=0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSever Motor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026chi;\u003csup\u003e2\u003c/sup\u003e=4.140, df=4, Nagelkerke R\u003csup\u003e2\u003c/sup\u003e =0.061, p=0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5. Receiver operating characteristic-area under curve (AUC) analysis of the biomarkers in the prediction of severe Motor in PD patients.\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eCI: Confidence interval.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"791\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrimination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden\u0026apos;s J statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC(CI 95%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003ePD vs. Controls\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHMGB1 pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3009.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e68.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e68.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.72(0.62-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRSPO1 pm/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e238.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e58.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.64(0.54-0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDKK1 ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e17.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.81(0.74-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eT.Ca mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.71(0.60-0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIonized Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e64.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.70(0.61-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSclerostin, Albumin, T. \u0026amp; I.Mg, T.Ca/Mg, and Ionized Ca/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026lt;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eSevere nonmotor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eRSPO1 pm/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e254.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.63(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDKK1, HMGB1, Sclerostin, Albumin, T. \u0026amp; I.Mg, T.\u0026amp; I.Ca, T.Ca/Mg, and Ionized Ca/Mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026lt;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003eSevere motor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAll biomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026lt;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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 disease, Wnt pathway, Neuroinflammation, Motor symptoms, Nonmotor symptoms","lastPublishedDoi":"10.21203/rs.3.rs-7919099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7919099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDysregulation of Wnt signaling and neuroinflammation are critically implicated in Parkinson’s disease (PD) pathogenesis. This study investigates the clinical utility of key circulating biomarkers related to these pathways for diagnosing PD and correlating with symptom severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this case-control study, 90 PD patients and 45 healthy controls (HC) were recruited. Serum levels of Wnt-related proteins (DKK1, Sclerostin, RSPO1), HMGB1, and electrolytes were measured using ELISA and colorimetric assays. Participants underwent comprehensive motor (MDS-UPDRS, Hoehn \u0026amp; Yahr) and non-motor (Fibro-Fatigue, NMS Scale) assessments. Statistical analyses included multivariate general linear models, partial correlations, binary logistic regression, and receiver operating characteristic (ROC) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum levels of DKK1 and HMGB1 were significantly elevated in the PD group compared to HC (p \u0026lt; 0.001). Binary logistic regression identified both as independent predictors of PD (DKK1: OR = 1.280, p \u0026lt; 0.001; HMGB1: OR = 1.000, p = 0.003). ROC analysis confirmed their strong diagnostic accuracy (DKK1 AUC = 0.81; HMGB1 AUC = 0.72). Within the PD cohort, HMGB1 correlated with disease progression (Hoehn \u0026amp; Yahr: r = 0.424, p \u0026lt; 0.01), while RSPO1 correlated with worse motor experiences of daily living (r = 0.286, p = 0.010) and Sclerostin with motor complications (r = 0.229, p = 0.033). However, no biomarker predicted severe motor or non-motor symptom severity in dedicated regression and ROC models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDKK1 and HMGB1 are robust diagnostic biomarkers for PD, underscoring the roles of Wnt dysregulation and neuroinflammation. The correlation of Sclerostin and RSPO1 with specific symptom domains suggests their function as disease modulators rather than diagnostic markers. This panel differentiates between biomarkers for diagnosis and those associated with symptom expression.\u003c/p\u003e","manuscriptTitle":"Dysregulated Wnt Signaling in Parkinson’s Disease: Correlation with Motor and Nonmotor Symptom Severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 02:18:24","doi":"10.21203/rs.3.rs-7919099/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":"9def2c35-1927-4d22-9c4d-34940e38bb62","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T10:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 02:18:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7919099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7919099","identity":"rs-7919099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}