Serum LDH and NLR as Diagnostic Biomarkers for Drooling in Parkinson's Disease

Serum LDH and NLR as Diagnostic Biomarkers for Drooling in Parkinson's Disease | 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 Serum LDH and NLR as Diagnostic Biomarkers for Drooling in Parkinson's Disease Xi-Xi Wang, Jia-Liang Shi, Hui-Hui Jin, Qin Gao, Qing Huang, Liang Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311117/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Drooling, a prevalent non-motor symptom in Parkinson’s disease (PD), lacks standardized diagnostic biomarkers. This study investigated the association between systemic inflammatory markers—serum neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII) and serum lactate dehydrogenase (LDH)—and drooling in PD. Materials and methods A total of 127 PD patients and 84 healthy controls (HC) were recruited. PD patients were divided into drooling (PD-DR, n = 62) and non-drooling (PD-NDR, n = 65) groups. Hematological parameters, clinical scales, and neuropsychological assessments were analyzed. Statistical analysis was performed to compare clinical characteristics, logistic regression for risk factors, receiver operating characteristic (ROC) curves for diagnostic accuracy, and restricted cubic splines (RCS) for nonlinear associations. Results The PD-DR group manifested significantly elevated levels of NLR, SII, and LDH compared to HC (p < 0.001). Furthermore, PD-DR patients also exhibited significantly elevated NLR ( p = 0.011) and LDH ( p = 0.004) levels when compared to PD-NDR patients. Multivariate logistic regression identified LDH levels (OR = 1.014, 95% CI 1.000-1.027, p = 0.043) as independent risk factors for drooling in PD patients. ROC analysis indicated that a serum LDH level of 174.50 mmol/L could differentiate between PD patients with and without drooling with an AUC of 0.662, sensitivity of 58.10%, and specificity of 70.80%. RCS analysis revealed a linear relationship between LDH and drooling severity ( p = 0.032) and a nonlinear "U"-shaped association for NLR ( p = 0.048). Conclusions This study identifies serum LDH and NLR as novel biomarkers correlating with drooling severity in PD, highlighting LDH as an independent risk factor. Clinical relevance: Monitoring LDH/NLR dynamics offers a cost-effective strategy for early detection and management of drooling severity in Parkinson's disease. Parkinson’s disease drooling inflammation lactate dehydrogenase neutrophil-to-lymphocyte ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Drooling, commonly known as sialorrhea, is a prevalent non-motor symptom associated with Parkinson's disease (PD), with reported prevalence rates in studies varying widely, from 10–84% [ 1 ]. Drooling manifests as excessive accumulation and overflow of saliva in the mouth, which may be caused by excessive saliva secretion or abnormal saliva clearance [ 2 ]. In individuals with PD, drooling embarrasses patients' lives and social interactions, exacerbates emotional and psychological issues, impairs speech and eating, damages the skin around the oral region, and even increases the risk of aspiration pneumonia and mortality[ 3 ]. However, the intricate mechanisms underlying drooling in PD remain not completely comprehended. Currently, drooling has been attributed to motor dysfunction caused by PD, which leads to impaired control of oral muscles and compromised swallowing reflexes [ 4 , 5 ]. Impaired control of tongue muscle and bradykinesia might exacerbate the pathophysiology of dysphagia and directly contribute to drooling. Additionally, reduced facial expression, involuntary mouth opening, a hunched posture, and a lowered head position can hinder an individual's capacity to retain saliva within the mouth, thereby leading to drooling in PD. Recent researches indicated that inflammation might significantly contribute to the pathogenesis of PD [ 6 – 8 ], including in the development of non-motor symptoms [ 9 , 10 ]. Studies indicated that immune-reactivity imbalances in both the peripheral system and the brain can lead to the upregulation of inflammatory cytokines, and then abnormally triggers a series of pro-inflammatory signaling events, which subsequently culminate in the neurotoxicity in PD [ 7 ]. The systemic immune-inflammation Index (SII) [ 11 ] and the Neutrophil-to-lymphocyte Ratio (NLR) [ 12 ] act as peripheral compound immunoinflammatory indexes that comprehensively reflect the status of systemic inflammation. The SII reflects the immune status of the body by assessing the ratio of leukocyte subsets in peripheral blood, while the NLR assesses the degree of inflammation by the ratio of neutrophils to lymphocytes. In PD patients, these indicators may be associated with the increased risk and progression of the disease [ 10 , 13 – 15 ]. Peripheral inflammation can facilitate the translocation of inflammatory cells and mediators across the blood-brain barrier into the central nervous system, thereby activating microglia and instigating neuroinflammation [ 16 ]. Thus, this inflammatory cascade might impair dopaminergic neuronal function, subsequently impacting oral and facial muscle movements as well as swallowing abilities in PD patients, ultimately resulting in increased drooling. As of now, the clinical manifestations of drooling in PD have frequently been neglected and suboptimally managed within medical practice. The current international landscape is marked by an absence of standardized and globally recognized diagnostic and evaluative instruments specifically for PD-related drooling, a gap that urgently requires bridging. Furthermore, there is a concerted effort to identify diagnostic biomarkers for drooling in PD, which is crucial for establishing a scientific framework to guide the development of personalized, targeted, and efficacious therapeutic interventions. Although studies have investigated the relationship between inflammatory factors and PD, direct evidence connecting these factors to drooling is still lacking. Therefore, this study aims to investigate the occurrence of drooling in PD patients and its correlation with the levels of inflammatory factors. This study may provide novel insight into the underlying mechanisms of drooling in PD and contribute to identifying potential diagnostic biomarkers. Materials and methods Participants A total of 356 individuals were recruited from the department of neurology at Nanjing First Hospital, Nanjing Medicial University between September 2021 and December 2024. Of these, 138 participants were excluded for not fulfilling the eligibility criteria, and 7 were excluded due to incomplete evaluations. The study ultimately comprised 127 individuals with PD and 84 healthy controls (HC). The PD group was further categorized into two subgroups: those with drooling (PD-DR, n = 62) and those without drooling (PD-NDR, n = 65), based on the Movement Disorder Society-Sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS) [ 17 ] part II-item 2 cutoff score of 0 (Fig. 1 ). Patients initially underwent a clinical assessment by two neurologists, both of whom were specialized in movement disorders and experienced in identifying psychiatric conditions associated with PD. Inclusion criteria: 1) ≥ 18 years; 2) A clinical diagnosis of idiopathic Parkinson's disease (IPD) in accordance with the MDS diagnostic criteria for PD; 3) Capable of completing all scale assessments with medical guidance. Exclusion criteria: 1)atypical or secondary parkinsonism; 2) Other neurological conditions or injuries (including traumatic brain injury, ischemic or hemorrhagic stroke, epilepsy, demyelinating disease, myopathy); 3) Unstable psychiatric disorders, such as schizophrenia or major depression or anxiety; 4) Significant cognitive deterioration, as indicated by a Mini-Mental State Examination (MMSE) [ 18 ] score of less than 24; 5) Systemic diseases impacting the function of the heart, lungs, liver, kidneys, and other organs that could influence drooling; 5) Acute or chronic infection; 6) Diabetes, hypothyroidism, tumors, hematologic disorders and autoimmune diseases; 7) Inability to comply with study requirements or exhibit poor adherence. Neuropsychological assessment Demographic data, including age, gender, and body mass index (BMI), were collected for all participants. The onset age and disease duration were documented for each individual with PD. Additionally, the levodopa-equivalent daily dose (LEDD) [ 19 ] was calculated for each patient. All PD patients underwent clinical assessments using standardized scales. Disease and motor severity were assessed in PD patients using the Hoehn and Yahr (H-Y) scale and the MDS-UPDRS part III. The effects of motor and non-motor symptoms on daily living were measured by the MDS-UPDRS parts I and II, respectively. Motor complications were assessed with the MDS-UPDRS part IV. Non-motor symptoms were quantified using the Non-Motor Symptom Scale (NMSS), considering both total and domain scores. Cognitive function was assessed using the MMSE and the Montreal Cognitive Assessment (MoCA). Anxiety and depression levels were determined using the Hamilton Anxiety Scale (HAMA) and the Hamilton Depression Scale (HAMD), respectively. Drooling was evaluated with the Drooling Clinical Scale for Parkinson's disease (SCS-PD) [ 20 ]. All evaluations were performed with participants in the "Off" state, requiring participants to cease dopamine receptor agonist intake 72 hours and discontinue other anti-Parkinson's disease medications 12 hours before assessment. Hematology Testing Approaches We documented the fundamental details of each participant, including their name, gender, and age. Blood specimens were obtained from all patients at 7:30 a.m. after a 12-hour fast. For routine hematological assessments, 2 milliliters of EDTA-anticoagulated whole blood was utilized. Additionally, 5 milliliters of blood containing coagulant was employed for conventional biochemical analyses. All assays were performed utilizing commercial kits, with qualified personnel following the protocols specified by the manufacturers. The neutrophil (NEU), lymphocyte (LYM), and platelet (PLT) counts were determined using an automated SYSMEX XN series and MINDRAY BC series five-part differential blood cell analyzers manufactured in Japan. The neutrophil-to-lymphocyte ratio (NLR; NEU/LYM) and systemic inflammation index (SII; PLT×NEU/LYM) were calculated. Furthermore, lactate dehydrogenase (LDH) serum levels were assessed utilizing the enzyme rate method on a BECKMAN COULTER AU5800 automated biochemical analyzer. Statistical analysis The data analyses were performed using SPSS Version 26.0. Data distribution and normality were measured using the Shapiro-Wilk test. Normality distribution data are presented as the mean ± standard deviation (mean ± SD). Skewness distribution data are presented as medians with interquartile ranges. Two-tailed t-test or Mann-Whitney U test was used for comparison between the two groups. For multi-group comparison, parametric data were analyzed by One-way ANOVA post hoc multiple comparisons with Tukey correction, and non-parametric data were analyzed by Kruskal-Wallis test post hoc multiple comparisons with Bonferroni correction. The chi-square test was performed on binary variables. Univariate logistic regression analyses were used to identify risk factors associated with drooling in PD patients. After that, factors with significant differences ( p < 0.05) were included as covariates in the multivariate logistic regression analysis to further explore their relationship with drooling in PD. The effects were expressed in terms of odds ratios (OR) and their respective 95% confidence intervals (CI). A receiver operating characteristic (ROC) curve was used to estimate the sensitivity, and specificity of predictive factors for the occurrence of drooling in PD patients. Furthermore, we conducted Restricted Cubic Spline analysis (RCS) in R version 4.3.3 to investigate the correlation between the severity of drooling in PD patients and the factors and used cloud-rain plot plots to illustrate the differences among the groups. p value < 0.05 was considered to be statistically significant. Results Clinical and demographic characteristics of PD patients and HC Initially, 356 candidate patients with PD were recruited. After exclusion of those who did not meet the inclusion criteria or had incomplete data, a total of 127 individuals with PD and 84 HC were enrolled in this study. Among the 127 PD patients, 52.8% were male and 47.2% were female, with an average age of 65.18 ± 10.09 years. The median BMI was 23.38kg/m 2 (21.30-25.61). The median age at onset of PD was 62.31 years (54.22–68.34). The median disease duration was 3.67 years (1.99–6.02). The median H-Y stage was 2.00 (2.00-2.50), and the median LEDD was 300.00 mg/day (175.00-450.00). No significant differences in age and gender were observed between the PD groups and HC ( p > 0.05, Table 1 ). However, PD patients exhibited a significantly lower BMI and markedly higher NLR ( p < 0.001), SII ( p = 0.001), and LDH ( p < 0.001) levels compared to the HC (Table 1 , Fig. 2 ). Additional demographic details are presented in Table 1 . Table 1 Clinicodemographic characteristics of study subjects. Characteristics PD (n = 127) HC (n = 84) PD-DR (n = 62) PD-NDR (n = 65) p value 1 p value 2 p value 3 p value 4 p value 5 Age (years )a 65.18 ± 10.09 64.08 ± 10.35 67.58 ± 8.53 62.88 ± 10.97 0.445 0.025* 0.025* 0.096 0.751 Gender (M/F) 67/60 39/45 34/28 33/32 0.368 0.601 0.646 0.315 0.599 BMI b 23.38(21.30-25.61) 24.12(22.86–26.81) 23.35(21.18–25.77) 23.44(21.31–25.39) 0.011* 0.039* 1.000 0.076 0.115 Age of onset (years) c 62.31(54.22–68.34) - 62.38 ± 8.15 58.87 ± 10.83 - - 0.040* Disease duration (years) d 3.67(1.99–6.02) - 4.48(2.55–6.68) 3.07(1.44–5.49) - - 0.016* LEDD (mg/day) d 300.00(175.00-450.00) - 312.50(200.00-481.25) 300.00(143.75–450.00) - - 0.106 H-Y stage d 2.00(2.00-2.50) - 2.00(2.00-2.50) 2.00(2.00-2.50) - - 0.081 MDS-UPDRS score d 45.00(34.00–56.00) - 48.50(41.75–66.50) 37.00(28.00-51.50) - - 0.000** MDS-UPDRS I score d 6.00(3.00–10.00) - 7.50(3.00–11.00) 5.00(2.50-8.00) - - 0.066 MDS-UPDRS II score d 10.00(5.00–13.00) - 12.00(8.00–15.00) 6.00(3.00–10.00) - - 0.000** MDS-UPDRS III score d 28.00(23.00–38.00) - 30.00(24.75–41.50) 26.00(17.50–35.50) - - 0.014* MDS-UPDRS IV score d 0.00(0.00–0.00) - 0.00(0.00–0.00) 0.00(0.00–0.00) - - 0.884 NMSS score d 20.00(13.00–33.00) - 27.00(14.50–42.00) 18.00(10.00–26.00) - - 0.004** NMSS-19 domain score d 0.00(0.00–0.00) - 0.00(0.00–3.00) 0.00(0.00–0.00) - - 0.000** SCS-PD score d 1.00(0.00–3.00) - 3.00(2.00–7.00) 0.00(0.00–0.00) - - 0.000** MMSE score d 28.00(26.00–29.00) - 28.00(25.00–29.00) 28.00(26.50–29.00) - - 0.17 MOCA score d 23.00(18.00–25.00) - 22.50(18.00–25.00) 24.00(18.50–27.00) - - 0.023* HAMD score d 5.00(2.00–10.00) - 5.50(2.75-11.00) 5.00(1.00-9.50) - - 0.462 HAMA score d 5.00(2.00–10.00) - 7.00(3.00-10.25) 4.00(2.00–8.00) - - 0.073 NLR b 2.30(1.59–2.80) 1.64(1.35–2.05) 2.47(2.02–3.05) 2.01(1.42–2.67) 0.000** 0.000** 0.011* 0.000** 0.021* SII b 404.04(290.61-563.06) 312.49(241.83-414.54) 463.81(316.98-658.39) 366.21(269.49-508.24) 0.001** 0.000** 0.078 0.000** 0.276 LDH b 171.00(156.00-191.00) 158.15(145.00-176.50) 179.00(164.75-208.25) 165(141.50–187.00) 0.000** 0.000** 0.004** 0.000** 0.505 *Indicates significant difference; **Indicates extremely significant difference. a One-way ANOVA post hoc multiple comparisons with Tukey correction. b Kruskal-Wallis test post hoc multiple comparisons with Bonferroni correction. c Two-tailed t-test:PD-DR versus PD-NDR. 1 Two-tailed t-test or Chi-square test or Mann-Whitney U test:PD versus HC. 2 One-way ANOVA or Chi-square test or Kruskal-Wallis test: Comparison among PD-DR, PD-NDR and HC . 3 Post hoc multiple comparisons:PD-DR versus PD-NDR. 4 Post hoc multiple comparisons: PD-DR versus HC. 5 Post hoc multiple comparisons:PD-NDR versus HC. PD, Parkinson's disease; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; HCs, healthy controls; BMI, Body Mass Index; LEDD, levodopa equivalent daily dose; H-Y stage, modified Hoehn-Yahr stage; MDS-UPDRS score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale total score; MDS-UPDRS I score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part I score; MDS-UPDRS II score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part II score; MDS-UPDRS III score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III score; MDS-UPDRS IV score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part IV score; NMSS score, Non-Motor Symptom Scale score; SCS-PD score, Sialorrhea Clinical Scale for Parkinson's disease score; MMSE score, Mini-Mental State Examination score; MOCA score, Montreal Cognitive Assessment score; HAMD score, Hamilton Depression Scale score; HAMA score, Hamilton Anxiety Scale score; NLR, neutrophilocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; LDH, lactate dehydrogenase. Group comparisons between PD-DR patients, PD-NDR patients and HC As stated previously, patients were divided based on MDS-UPDRS part II-item 2 into either the PD-DR group (n = 62) or the PD-NDR group (n = 65). The Chi-square test indicates that there were no significant differences in gender among PD-DR, PD-NDR and HC ( p > 0.05). The One-way ANOVA indicates that there were significant differences in age and BMI among PD-DR, PD-NDR and HC ( p = 0.039). The Kruskal-Wallis test revealed highly significant differences in NLR, SII, and LDH levels among the PD-DR group, PD-NDR group, and HC ( p < 0.001) (Table 1 , Fig. 3 ). Notably, post hoc analyses within the PD-DR group showed significantly higher NMSS-19 domain scores ( p < 0.001) and SCS-PD scores ( p < 0.001) compared to the PD-NDR group (Table 1 ). No significant differences were observed between the PD-DR group and the PD-NDR group in terms of gender, BMI, LEDD, H-Y stage, MDS-UPDRS I score, MDS-UPDRS IV score, MMSE score, HAMD score, and HAMA score ( p > 0.05) (Table 1 ). Nevertheless, the PD-DR group exhibited significantly higher values in age ( p = 0.025), age of onset ( p = 0.040), disease duration ( p = 0.016), MDS-UPDRS total score ( p < 0.001), MDS-UPDRS II score ( p < 0.001), MDS-UPDRS III score ( p = 0.014), NMSS score ( p = 0.004), and MoCA score ( p = 0.023) compared to the PD-NDR group (Table 1 ). Besides, the PD-DR group had markedly higher levels of NLR ( p = 0.011) and LDH ( p = 0.004) than the PD-NDR group (Table 1 ). In contrast, there was no significant variation in SII between the groups ( p > 0.05) (Table 1 ). Post-hoc multiple comparisons revealed that there were no statistically significant differences in age, gender, or BMI between the PD-DR group and HC ( p > 0.05), while the PD-DR group manifested significantly elevated levels of NLR, SII, and LDH compared to HC ( p 0.05), whereas the NLR level in the PD-NDR group was significantly higher than that in HC ( p = 0.021) (Table 1 ). Univariate and Multivariate Logistic Regression Analysis Univariate logistic regression analyses between the PD-DR and PD-NDR groups revealed that age ( p = 0.010), age of onset ( p = 0.044), MDS-UPDRS III scores ( p = 0.022), MOCA scores ( p = 0.044), and LDH levels ( p = 0.004) significantly correlate with drooling in PD. Subsequently, these significant factors were incorporated as covariates into the subsequent multivariate logistic regression analysis. The findings indicated that: both in the PD-DR group and the PD-NDR group, individuals with elevated LDH levels exhibited an increased likelihood of experiencing drooling, which reached statistical significance (OR = 1.014, 95% CI 1.000-1.027, p = 0.043) (Table 2 ). As such means that for each unit increase in LDH levels, the odds of experiencing drooling in PD patients increased by 1.4% while holding the other variables constant. Table 2 Univariate and multivariate logistic regression analysis to identify risk factors associated with drooling in PD patients. Potential risk factor Univariate analysis Multivariate analysis OR (95%CI) p value OR (95%CI) p value Between PD-DR and PD-NDR Age (years) 1.050 (1.012–1.090) 0.010* 1.060 (0.948–1.185) 0.309 Gender (M/F) 0.849 (0.423–1.706) 0.646 BMI 1.015 (0.918–1.123) 0.770 Age of onset (years) 1.039 (1.001–1.079) 0.044* 0.966 (0.865–1.079) 0.542 Disease duration (years) 1.100 (0.992–1.220) 0.071 LEDD (mg/day) 1.001 (1.000-1.003) 0.090 H-Y stage 1.780 (0.995–3.185) 0.052 MDS-UPDRS I score 1.073 (1.000-1.152) 0.051 MDS-UPDRS III score 1.034 (1.005–1.063) 0.022* 1.014 (0.981–1.048) 0.416 MDS-UPDRS IV score 1.013 (0.883–1.164) 0.851 MMSE score 0.927 (0.842–1.020) 0.119 MOCA score 0.934 (0.875–0.998) 0.044* 0.968 (0.896–1.045) 0.400 HAMD score 1.027 (0.968–1.089) 0.380 HAMA score 1.052 (0.992–1.116) 0.091 NLR 1.340 (0.996–1.082) 0.053 SII 1.001 (1.000-1.003) 0.055 LDH 1.019 (1.006–1.031) 0.004* 1.014 (1.000-1.027) 0.043* Between PD-DR and HC Age (years) 1.039 (1.003–1.077) 0.034* 1.011 (0.968–1.057) 0.614 BMI 0.920 (0.828–1.022) 0.121 NLR 4.664 (2.495–8.718) 0.000* 7.583 (2.550-22.547) 0.000* SII 1.004 (1.002–1.006) 0.000* 0.998 (0.994–1.001) 0.212 LDH 1.034 (1.018–1.049) 0.000* 1.032 (1.015–1.049) 0.000* *Indicates significant difference. PD, Parkinson's disease; OR, Odds ratio; CI, confidence interval; BMI, Body mass index; LEDD, Levodopa equivalent daily dose; H-Y stage, modified Hoehn-Yahr stage; MDS-UPDRS I score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part I score; MDS-UPDRS III score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III score; MMSE score, Mini-Mental State Examination score; MOCA score, Montreal Cognitive Assessment score; HAMD score, Hamilton Depression Scale score; HAMA score, Hamilton Anxiety Scale score; NLR, neutrophilocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; LDH, lactate dehydrogenase. Additionally, univariate logistic regression analyses comparing the PD-DR to HC demonstrated that age ( p = 0.034), NLR levels ( p < 0.001), SII levels ( p < 0.001) and LDH levels ( p < 0.001) significantly correlate with drooling in PD. Subsequently, these significant factors were incorporated as covariates into the subsequent multivariate logistic regression analysis. The findings indicated that: both in the PD-DR subgroup and the HC group, an augmented NLR value correlated with a heightened risk of drooling in PD, with the difference being statistically significant (OR = 7.583, 95% CI 2.550-22.547, p < 0.001) (Table 2 ). For example, if NLR levels increased by 1 unit, the odds of drooling in PD patients would be 7.583 times higher, assuming other variables remain unchanged. This indicates that NLR could be a strong predictor of drooling in PD. Additionally, a higher LDH level was associated with an increased risk of drooling in PD patients, and this association was also statistically significant (OR = 1.032, 95% CI 1.015–1.049, p = 0.043) (Table 2 ). Similar to the previous comparison, this implies that for each unit increase in LDH levels, the odds of drooling increased by 3.2%. ROC Analysis Utilizing ROC curves, a serum LDH level of 174.50mmol/L was found to differentiate between PD patients with and without drooling to a certain extent, with an AUC curve of 0.662, which translates to a sensitivity of 58.10% and a specificity of 70.80% (Table 3 , Fig. 4 A). Table 3 The AUC, 95% CI, P. value, optimal cutoff value, sensitivity, specificity, and youden index of LDH in discriminating PD-DR (n = 62) and from PD-NDR (n = 65). DELncRNA AUC 95% CI p value Optimal cutoff value Sensitivity Specificity Youden index LDH 0.662 0.567–0.756 0.002* 174.50 0.581 0.708 0.288 *Significant difference, p value < 0.05. ROC, receiver operating characteristic; CI, Confidence interval; DElncRNA, differentially expressed long non-coding RNA; AUC, area under the ROC curve; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; LDH, lactate dehydrogenase. Utilizing ROC curves, a serum NLR level of 2.05 could potentially differentiate PD patients with drooling from HC, with an area under the AUC curve of 0.785, indicating a sensitivity of 74.20% and a specificity of 77.40% (Table 4 , Fig. 4 B). Similarly, a serum LDH level of 163.79mmol/L could distinguish PD patients with drooling from HC, with an AUC of 0.732, corresponding to a sensitivity of 79.00% and a specificity of 60.70% (Table 4 , Fig. 4 B). Furthermore, combining serum NLR levels with LDH levels could enhance the differentiation of PD patients with drooling from HC, achieving an AUC of 0.835. When the cut-off value was 0.47, it corresponds to a sensitivity of 71.00% and a specificity of 83.30% (Table 4 , Fig. 4 B). Table 4 The AUC, 95% CI, P. value, optimal cutoff value, sensitivity, specificity, and youden index of NLR, LDH and NLR combined with LDH in discriminating PD-DR (n = 62) and from HC (n = 84). DELncRNA AUC 95% CI p value Optimal cutoff value Sensitivity Specificity Youden index NLR 0.785 0.709–0.861 0.000* 2.05 0.742 0.774 0.516 LDH 0.732 0.650–0.814 0.000* 163.79 0.790 0.607 0.397 NLR + LDH 0.835 0.768–0.901 0.000* 0.47 0.710 0.833 0.543 *Significant difference, p value < 0.05. ROC, receiver operating characteristic; CI, Confidence interval; DElncRNA, differentially expressed long non-coding RNA; AUC, area under the ROC curve; PD-DR, PD patients with drooling; HC, healthy control; NLR, neutrophilocyte-to-lymphocyte ratio; LDH, lactate dehydrogenase. RCS Analysis Employing an RCS-fitted linear regression model, and adjusting for variables such as age, gender and BMI, we examined the correlation between LDH levels, NLR levels, and SCS scores within the PD-DR subgroup. The analysis revealed a significant linear association between LDH levels and SCS scores ( p for overall = 0.032, p for nonlinear = 0.077), indicating a positive correlation where an increase in LDH levels is accompanied by a significant rise in SCS scores, as illustrated in Fig. 5 A. Conversely, a significant nonlinear "U"-shaped relationship was observed between NLR levels and SCS scores ( p for overall = 0.057, p for nonlinear = 0.048). The NLR level of the lowest point of SCS score is about 2.47, and when the NLR level is less than 2.47, the SCS score gradually decreases with the increase of NLR level. When the NLR level exceeded 2.47, the SCS score increased with the increase of the NLR level, as shown in Fig. 5 B. Discussion Inflammation is increasingly recognized as a key factor in the pathological features and symptoms of PD [ 21 ]. Preclinical and clinical studies in PD patients have shown evidence of elevated inflammatory responses in the central and peripheral systems, such as microglial activation [ 21 ], as well as increased levels of immune and inflammatory markers in the brain and peripheral blood [ 7 ]. However, there is a lack of studies exploring the connection between these immune and inflammatory factors and drooling in individuals with PD. Notably, our study found that serum NLR, SII, and LDH levels were significantly elevated in PD patients. The increased serum NLR and LDH levels were associated with a heightened risk of drooling in PD, and both, as well as their combination, can serve as potential blood biomarkers for the diagnosis of PD-related drooling. Within the PD groups, the PD-DR group displayed significantly elevated NLR and LDH levels compared to the PD-NDR group, and the salivation symptoms became more severe with increasing LDH levels. Notably, we made a novel discovery that elevated LDH levels independently predict drooling in PD patients. Furthermore, through the ROC curve analysis, we also found that LDH has a significant predictive advantage over NLR and SII in forecasting the development of drooling in PD patients. This might suggest that LDH levels could serve as a new blood biomarker for drooling in PD patients and shed light on the underlying pathological mechanisms of drooling in PD. Studies [ 22 , 23 ]have shown that LDH inhibitors can reduce the release of pro-inflammatory cytokines (such as TNF-α and IL-6) from macrophages at inflamed sites and increase the release of the anti-inflammatory cytokine IL-10, indicating that LDH plays a significant role in regulating inflammatory responses. Additionally, LDH can regulate lymphocyte activity, and enhance T cell proliferation and cytotoxicity when exogenously added, while LDH inhibitors can block T cell activation and stabilize regulatory T cells, thereby influencing immune responses. Besides, the overexpression of hexokinase 2 and LDH-A, coupled with elevated lactate levels, contributes to the apoptosis of dopaminergic neurons in PD [ 24 ]. The LDH, a ubiquitous enzyme found in diverse bodily tissues, plays a pivotal role in catalyzing the interconversion between pyruvate and lactate, thereby modulating the systemic levels of lactic acid. Lactic acid has been found to enhance the aggregation of α-synuclein within neurons [ 25 ], thereby triggering cell death. Consequently, it is plausible that lactate, a byproduct of LDH-catalyzed glycolysis, plays a role in the apoptotic processes affecting dopaminergic neurons in PD. Besides, LDH is implicated in energy metabolism within cells, and alterations in its levels might indicate augmented cell damage or oxidative stress in the brain. Future research should further explore the role of LDH in PD, particularly in relation to drooling, and examine its interactive dynamics with a spectrum of other biomarkers. Our study demonstrated a significant nonlinear “U”-shaped curve relationship between the NLR and the severity of drooling within the PD-DR subgroup. After adjusting for confounding factors (age, gender, BMI), the analysis showed that when NLR was below 2.47, higher NLR correlated with less severe drooling; when NLR exceeded 2.47, higher NLR was linked to more severe drooling. The NLR, which integrates the counts of two key leukocyte subtypes—neutrophils and lymphocytes—serves as a reflective measure of the equilibrium between systemic inflammation and immune response. Neutrophils are indicative of chronic inflammatory conditions [ 26 ], while lymphocytes might embody the regulatory arm of the immune system [ 27 ]. The NLR has been suggested to be elevated in PD as a biomarker of peripheral inflammation [ 28 ], as observed in our study. At present, no research exists on NLR levels in PD patients with drooling. For the first time, we observed a significant increase in NLR levels in PD patients with drooling, and it may serve as a promising noninvasive diagnostic biomarker for PD patients with drooling. However, the precise mechanism of the NLR in PD patients with drooling remains elusive and warrants further investigation through cellular studies and animal models in future research. Similar to previous studies, we also found that SII levels were elevated in PD patients compared to HC. The SII, which combines three complementary cells (neutrophils, platelets, and lymphocytes) that regulate different inflammatory or immune pathways, is an objective marker of the balance between systemic inflammation and immune response within the host. It is worth mentioning that both NLR and SII were less affected by confounding conditions and were more predictive in assessing chronic systemic inflammatory status than neutrophils, platelets, or lymphocytes alone. Over the past few years, SII has been utilized to assess the short-term adverse outcomes in acute ischemic stroke [ 29 ] and to predict the prognosis of patients suffering from spontaneous cerebral hemorrhage [ 30 ]. Moreover, higher SII could increase the risk of mild cognitive impairment [ 31 ]. A recent study has identified SII as a significant factor influencing the motor function of PD patients, and the findings reveal a substantial negative correlation between SII levels and scores of Activities of Daily Living [ 32 ]. Although there was no statistically significant difference in SII between the PD-DR group and PD-NDR group, we could observe that the SII levels of PD-DR group were higher than that of PD-NDR group. We speculate that the observed results may be attributed to the sample size, and we intend to broaden our sample in future studies to delve into the variations of SII levels between the two groups. However, this study had several limitations: (1) the sample size was modest, which might result in selection bias; (2) given its cross-sectional design, the study could not capture the longitudinal progression, and thus, further research is required to determine the potential of these markers as prognostic indicators; (3) future studies should consider integrating multi-dimensional indices, such as neuroimaging, neuro-electrophysiology and cerebrospinal fluid analysis, to achieve a more comprehensive understanding of the pathological processes and to enhance the predictive accuracy of biomarkers for PD patients with drooling; (4) this study's inflammatory indicators were limited to terminal markers in inflammatory pathways. Key molecular markers along these pathways, including cytokines, chemokines and complement pathway molecules, which could potentially yield further insights, were not measured; (5) this study did not include C-reactive protein or high-sensitivity C-reactive protein, indicators of chronic inflammation more commonly used in clinical settings. Future research should ideally incorporate these elements; (6) the underlying mechanisms of how blood markers are associated with salivation in PD, as identified in this study, are not yet understood and warrant further investigation through methods like in vitro cytological studies and experiments using animal models. Conclusions Our research indicated a correlation between LDH, NLR levels and the severity of drooling in PD, and identified LDH as an independent risk factor for drooling in PD, serving as a novel peripheral blood biomarker for predicting drooling in PD patients. Monitoring the dynamic changes in peripheral blood LDH and NLR levels upon admission is a simple and cost-effective method. It not only aids in the early detection of drooling symptoms in PD patients, providing timely clues for diagnosis and prognosis, and assisting in prevention and treatment, but also contributes to the study of how immune-inflammatory responses are involved in neurodegenerative diseases. Additional studies are required to confirm these findings and to deepen our understanding of the underlying mechanisms of drooling in PD. Declarations Author contributions Study design and manuscript draft: Ying-Dong Zhang, Ting Huang, You-Yong Tian, Xi-Xi Wang, Jia-Liang Shi; recruitment of subjects and data collection: Ting Huang, Xi-Xi Wang, Jia-Liang Shi; data analysis and discussion: Ting Huang, Xi-Xi Wang, Jia-Liang Shi, Hui-hui Jin, Qin Gao, Qing Huang, Liang Wu. All authors have read and approved the final manuscript. Funding This work was supported by the National Science and Technology Innovation 2030 - Major program of "Brain Science and Brain-Inspired Intelligence Research" (Grant No.2021ZD0201807). Data availability Data generated during this study are available upon reasonable request from the corresponding author. Requests for data should be directed to [ [email protected] ], and will be considered in accordance with relevant data-sharing policies and ethical guidelines. Ethics approval statement This study was approved by the Ethics Committee of Nanjing First Hospital (No.KY20220124-06). Competing interests The authors declare no competing interests. Consent statement All participants provided informed consent prior to their involvement in the study. References Srivanitchapoom P, Pandey S, Hallett M (2014) Drooling in Parkinson's disease: a review. Parkinsonism Relat Disord 20:1109–1118. https://doi.org/10.1016/j.parkreldis.2014.08.013 Zlotnik Y, Balash Y, Korczyn AD, Giladi N, Gurevich T (2015) Disorders of the oral cavity in Parkinson's disease and parkinsonian syndromes. Parkinsons Dis 2015:379482. https://doi.org/10.1155/2015/379482 Kalf JG, Smit AM, Bloem BR, Zwarts MJ, Munneke M (2007) Impact of drooling in Parkinson's disease. J Neurol 254:1227–1232. https://doi.org/10.1007/s00415-007-0508-9 Nóbrega AC, Rodrigues B, Torres AC, Scarpel RD, Neves CA, Melo A (2008) Is drooling secondary to a swallowing disorder in patients with Parkinson's disease? Parkinsonism Relat Disord 14:243–245. https://doi.org/10.1016/j.parkreldis.2007.08.003 Kalf JG, Munneke M, van den Engel-Hoek L, de Swart BJ, Borm GF, Bloem BR, Zwarts MJ (2011) Pathophysiology of diurnal drooling in Parkinson's disease. Mov Disord 26:1670–1676. https://doi.org/10.1002/mds.23720 Araújo B, Caridade-Silva R, Soares-Guedes C, Martins-Macedo J, Gomes ED, Monteiro S, Teixeira FG (2022) Neuroinflammation and Parkinson's Disease-From Neurodegeneration to Therapeutic Opportunities. https://doi.org/10.3390/cells11182908 . Cells 11 Tansey MG, Wallings RL, Houser MC, Herrick MK, Keating CE, Joers V (2022) Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol 22:657–673. https://doi.org/10.1038/s41577-022-00684-6 Calabrese V, Santoro A, Monti D, Crupi R, Di Paola R, Latteri S, Cuzzocrea S, Zappia M, Giordano J, Calabrese EJ, Franceschi C (2018) Aging and Parkinson's Disease: Inflammaging, neuroinflammation and biological remodeling as key factors in pathogenesis. Free Radic Biol Med 115:80–91. https://doi.org/10.1016/j.freeradbiomed.2017.10.379 Lindqvist D, Hall S, Surova Y, Nielsen HM, Janelidze S, Brundin L, Hansson O (2013) Cerebrospinal fluid inflammatory markers in Parkinson's disease–associations with depression, fatigue, and cognitive impairment. Brain Behav Immun 33:183–189. https://doi.org/10.1016/j.bbi.2013.07.007 Wang LX, Liu C, Shao YQ, Jin H, Mao CJ, Chen J (2022) Peripheral blood inflammatory cytokines are associated with rapid eye movement sleep behavior disorder in Parkinson's disease. Neurosci Lett 782:136692. https://doi.org/10.1016/j.neulet.2022.136692 Mangoni AA, Zinellu A (2024) The diagnostic role of the systemic inflammation index in patients with immunological diseases: a systematic review and meta-analysis. Clin Exp Med 24:27. https://doi.org/10.1007/s10238-024-01294-3 Zahorec R (2021) Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl Lek Listy 122:474–488. https://doi.org/10.4149/bll_2021_078 Zhong X, Qiang Y, Wang L, Zhang Y, Li J, Feng J, Cheng W, Tan L, Yu J (2023) Peripheral immunity and risk of incident brain disorders: a prospective cohort study of 161,968 participants. Transl Psychiatry 13:382. https://doi.org/10.1038/s41398-023-02683-0 Dommershuijsen LJ, Ruiter R, Erler NS, Rizopoulos D, Ikram MA, Ikram MK (2022) Peripheral Immune Cell Numbers and C-Reactive Protein in Parkinson's Disease: Results from a Population-Based Study. J Parkinsons Dis 12:667–678. https://doi.org/10.3233/jpd-212914 Kim R, Kang N, Byun K, Park K, Jun JS (2023) Prognostic significance of peripheral neutrophils and lymphocytes in early untreated Parkinson's disease: an 8-year follow-up study. J Neurol Neurosurg Psychiatry 94:1040–1046. https://doi.org/10.1136/jnnp-2022-330394 Lau K, Kotzur R, Richter F (2024) Blood-brain barrier alterations and their impact on Parkinson's disease pathogenesis and therapy. Transl Neurodegener 13:37. https://doi.org/10.1186/s40035-024-00430-z Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G (2015) MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30:1591–1601. https://doi.org/10.1002/mds.26424 Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198. https://doi.org/10.1016/0022-3956(75)90026-6 Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE (2010) Systematic review of levodopa dose equivalency reporting in Parkinson's disease. Mov Disord 25:2649–2653. https://doi.org/10.1002/mds.23429 Perez Lloret S, Pirán Arce G, Rossi M, Caivano Nemet ML, Salsamendi P, Merello M (2007) Validation of a new scale for the evaluation of sialorrhea in patients with Parkinson's disease. Mov Disord 22:107–111. https://doi.org/10.1002/mds.21152 Pajares M, A IR, Manda G, Boscá L, Cuadrado A (2020) Inflammation in Parkinson's Disease: Mechanisms and Therapeutic Implications. https://doi.org/10.3390/cells9071687 . Cells 9 Certo M, Tsai CH, Pucino V, Ho PC, Mauro C (2021) Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat Rev Immunol 21:151–161. https://doi.org/10.1038/s41577-020-0406-2 Manerba M, Di Ianni L, Govoni M, Roberti M, Recanatini M, Di Stefano G (2017) Lactate dehydrogenase inhibitors can reverse inflammation induced changes in colon cancer cells. Eur J Pharm Sci 96:37–44. https://doi.org/10.1016/j.ejps.2016.09.014 Li J, Chen L, Qin Q, Wang D, Zhao J, Gao H, Yuan X, Zhang J, Zou Y, Mao Z, Xiong Y, Min Z, Yan M, Wang CY, Xue Z (2022) Upregulated hexokinase 2 expression induces the apoptosis of dopaminergic neurons by promoting lactate production in Parkinson's disease. Neurobiol Dis 163:105605. https://doi.org/10.1016/j.nbd.2021.105605 Jiang P, Gan M, Ebrahim AS, Castanedes-Casey M, Dickson DW, Yen SH (2013) Adenosine monophosphate-activated protein kinase overactivation leads to accumulation of α-synuclein oligomers and decrease of neurites. Neurobiol Aging 34:1504–1515. https://doi.org/10.1016/j.neurobiolaging.2012.11.001 Soehnlein O, Steffens S, Hidalgo A, Weber C (2017) Neutrophils as protagonists and targets in chronic inflammation. Nat Rev Immunol 17:248–261. https://doi.org/10.1038/nri.2017.10 Tansey MG, Romero-Ramos M (2019) Immune system responses in Parkinson's disease: Early and dynamic. Eur J Neurosci 49:364–383. https://doi.org/10.1111/ejn.14290 Hosseini S, Shafiabadi N, Khanzadeh M, Ghaedi A, Ghorbanzadeh R, Azarhomayoun A, Bazrgar A, Pezeshki J, Bazrafshan H, Khanzadeh S (2023) Neutrophil to lymphocyte ratio in parkinson's disease: a systematic review and meta-analysis. BMC Neurol 23:333. https://doi.org/10.1186/s12883-023-03380-7 Zhou YX, Li WC, Xia SH, Xiang T, Tang C, Luo JL, Lin MJ, Xia XW, Wang WB (2022) Predictive Value of the Systemic Immune Inflammation Index for Adverse Outcomes in Patients With Acute Ischemic Stroke. Front Neurol 13:836595. https://doi.org/10.3389/fneur.2022.836595 Trifan G, Testai FD (2020) Systemic Immune-Inflammation (SII) index predicts poor outcome after spontaneous supratentorial intracerebral hemorrhage. J Stroke Cerebrovasc Dis 29:105057. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105057 Wang X, Li T, Li H, Li D, Wang X, Zhao A, Liang W, Xiao R, Xi Y (2022) Association of Dietary Inflammatory Potential with Blood Inflammation: The Prospective Markers on Mild Cognitive Impairment. Nutrients 14. https://doi.org/10.3390/nu14122417 Li S, Zhang Q, Gao Y, Nie K, Liang Y, Zhang Y, Wang L (2021) Serum Folate, Vitamin B12 Levels, and Systemic Immune-Inflammation Index Correlate With Motor Performance in Parkinson's Disease: A Cross-Sectional Study. Front Neurol 12:665075. https://doi.org/10.3389/fneur.2021.665075 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7311117","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501794047,"identity":"a570ff92-6227-4ffe-9ee8-1b59a414ffd4","order_by":0,"name":"Xi-Xi Wang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xi-Xi","middleName":"","lastName":"Wang","suffix":""},{"id":501794048,"identity":"ccb03007-045d-467b-a739-5d20422dd343","order_by":1,"name":"Jia-Liang Shi","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia-Liang","middleName":"","lastName":"Shi","suffix":""},{"id":501794049,"identity":"6c844664-2c39-4b6f-aaaf-01b76d886ce5","order_by":2,"name":"Hui-Hui Jin","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Hui","middleName":"","lastName":"Jin","suffix":""},{"id":501794050,"identity":"fb870e2c-fc87-4f93-bdb7-78f8dc4c7854","order_by":3,"name":"Qin Gao","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Gao","suffix":""},{"id":501794051,"identity":"88d0eb30-67ae-4c3a-baf4-4c828228bc30","order_by":4,"name":"Qing Huang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Huang","suffix":""},{"id":501794052,"identity":"ef855f57-8526-4210-bc7d-843c40b91309","order_by":5,"name":"Liang Wu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Wu","suffix":""},{"id":501794053,"identity":"b602f293-306a-40c8-985d-8549bb8c3b4a","order_by":6,"name":"You-Yong Tian","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"You-Yong","middleName":"","lastName":"Tian","suffix":""},{"id":501794054,"identity":"f9623657-d353-46d9-8caa-be75b8e3a305","order_by":7,"name":"Ting Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACAwglwcDAzHzwQUJFDbFaEoBa2NmSDR6cOUa0FiDm5zGTfNjCTFiLuUTys4dff1jkyTuzpVUkNrAx8Ld3J+DVYjkjzdxYJkGi2PAw87EbiTtkGCTOnN2A32E3EsykJRIkEjc2s6XdSDzDxmAgkUtIS/o3qBYes4LENmZitOSYSX4AapnPzGPGQJyWM2/KpBnSJBI3MLMlSyScOcZD2C/H07dJ/rCpS5zff/jgxx8VNXL87b34tYAAMw9I7wEIh4egchBg/AEk5BuIUjsKRsEoGAUjEQAA1FtIWnzz+XYAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ting","middleName":"","lastName":"Huang","suffix":""},{"id":501794055,"identity":"52564603-70a9-4f46-acfb-92392ec587c7","order_by":8,"name":"Ying-Dong Zhang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying-Dong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-08-06 15:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7311117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7311117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89973790,"identity":"5c061e6c-bd17-407c-b2a8-2aa054ca6669","added_by":"auto","created_at":"2025-08-27 05:52:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114859,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow chart. Abbreviations: PD, Parkinson's disease; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; HC, healthy control.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7311117/v1/b8a9c087e9b8a8d5bdbbb8c2.jpg"},{"id":89973788,"identity":"ec8ae2a1-7929-4c8b-a495-d4a738b70815","added_by":"auto","created_at":"2025-08-27 05:52:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62964,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in infammatory factors between PD and HC. A. The PD group shows increases in NLR compared to HC. B. The PD group shows increases in SII compared to HC. C. The PD group shows increases in LDH compared to HC. Abbreviations: PD, Parkinson's disease; HC, healthy control; NLR, neutrophilocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; LDH, lactate dehydrogenase.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7311117/v1/d72fa995446817024cfd5263.jpg"},{"id":89973795,"identity":"687b2cfd-2b4f-4dbf-b364-6e3b8956e420","added_by":"auto","created_at":"2025-08-27 05:52:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90327,"visible":true,"origin":"","legend":"\u003cp\u003eRaincloud plots depicting differences in inflammatory factors between PD-DR, PD-NDR and HC. A. The PD-DR group shows increases in NLR compared to PD-NDR and HC. B. The PD-DR group shows increases in SII compared to HC. C. The PD-DR group shows increases in LDH compared to PD-NDR and HC. Abbreviations: PD, Parkinson's disease; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; HC, healthy control; NLR, neutrophilocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; LDH, lactate dehydrogenase.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7311117/v1/eb1bd029d039000af35d3755.jpg"},{"id":89973803,"identity":"e6a292d3-aea8-41ea-8d4b-78a874a91310","added_by":"auto","created_at":"2025-08-27 05:52:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92875,"visible":true,"origin":"","legend":"\u003cp\u003eA. The diagnostic value of LDH in PD-DR. The red ROC curve of LDH for discriminating PD-DRfrom PD-NDR. The AUC was up to 0.662. B. The diagnostic value of NLR, LDH and NLR combined with LDHin PD-DR. The purple ROC curve of NLR for discriminating PD-DR from HC. The AUC was up to 0.785. The red ROC curve of LDH for discriminating PD-DR from HC. The AUC was up to 0.732. The blue ROC curve of NLR combined with LDH for discriminating PD-DR from HC. The AUC was up to 0.835. Abbreviations: PD, Parkinson's disease; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; HC, healthy control; ROC, receiver operating characteristic; AUC, area under the ROC curve.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7311117/v1/afe42140a301d3e232672b88.jpg"},{"id":89976098,"identity":"56960533-8b70-4ced-8746-3a4ad6d063c2","added_by":"auto","created_at":"2025-08-27 06:00:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56051,"visible":true,"origin":"","legend":"\u003cp\u003eThe restricted cubic spline of the association between the SCS-PD score and the LDH and NLR of PD-DR; The association was adjusted for age, gender, and body mass index. A. LDH; B. NLR. Abbreviations: PD-DR, PD patients with drooling; SCS-PD score, Sialorrhea Clinical Scale for Parkinson's disease score; LDH, lactate dehydrogenase; NLR, neutrophilocyte-to-lymphocyte ratio.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7311117/v1/07d3a6a4d0105a0f5189857e.jpg"},{"id":91441569,"identity":"b1845e17-b8b7-4c08-b843-bb4b2765cb66","added_by":"auto","created_at":"2025-09-16 14:09:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1464049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7311117/v1/6caf3d76-45ac-4ae4-8bd5-61fdb3f13e57.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum LDH and NLR as Diagnostic Biomarkers for Drooling in Parkinson's Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrooling, commonly known as sialorrhea, is a prevalent non-motor symptom associated with Parkinson's disease (PD), with reported prevalence rates in studies varying widely, from 10\u0026ndash;84% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Drooling manifests as excessive accumulation and overflow of saliva in the mouth, which may be caused by excessive saliva secretion or abnormal saliva clearance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In individuals with PD, drooling embarrasses patients' lives and social interactions, exacerbates emotional and psychological issues, impairs speech and eating, damages the skin around the oral region, and even increases the risk of aspiration pneumonia and mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the intricate mechanisms underlying drooling in PD remain not completely comprehended. Currently, drooling has been attributed to motor dysfunction caused by PD, which leads to impaired control of oral muscles and compromised swallowing reflexes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Impaired control of tongue muscle and bradykinesia might exacerbate the pathophysiology of dysphagia and directly contribute to drooling. Additionally, reduced facial expression, involuntary mouth opening, a hunched posture, and a lowered head position can hinder an individual's capacity to retain saliva within the mouth, thereby leading to drooling in PD.\u003c/p\u003e\u003cp\u003eRecent researches indicated that inflammation might significantly contribute to the pathogenesis of PD [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], including in the development of non-motor symptoms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies indicated that immune-reactivity imbalances in both the peripheral system and the brain can lead to the upregulation of inflammatory cytokines, and then abnormally triggers a series of pro-inflammatory signaling events, which subsequently culminate in the neurotoxicity in PD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The systemic immune-inflammation Index (SII) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and the Neutrophil-to-lymphocyte Ratio (NLR) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] act as peripheral compound immunoinflammatory indexes that comprehensively reflect the status of systemic inflammation. The SII reflects the immune status of the body by assessing the ratio of leukocyte subsets in peripheral blood, while the NLR assesses the degree of inflammation by the ratio of neutrophils to lymphocytes. In PD patients, these indicators may be associated with the increased risk and progression of the disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Peripheral inflammation can facilitate the translocation of inflammatory cells and mediators across the blood-brain barrier into the central nervous system, thereby activating microglia and instigating neuroinflammation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, this inflammatory cascade might impair dopaminergic neuronal function, subsequently impacting oral and facial muscle movements as well as swallowing abilities in PD patients, ultimately resulting in increased drooling.\u003c/p\u003e\u003cp\u003eAs of now, the clinical manifestations of drooling in PD have frequently been neglected and suboptimally managed within medical practice. The current international landscape is marked by an absence of standardized and globally recognized diagnostic and evaluative instruments specifically for PD-related drooling, a gap that urgently requires bridging. Furthermore, there is a concerted effort to identify diagnostic biomarkers for drooling in PD, which is crucial for establishing a scientific framework to guide the development of personalized, targeted, and efficacious therapeutic interventions. Although studies have investigated the relationship between inflammatory factors and PD, direct evidence connecting these factors to drooling is still lacking. Therefore, this study aims to investigate the occurrence of drooling in PD patients and its correlation with the levels of inflammatory factors. This study may provide novel insight into the underlying mechanisms of drooling in PD and contribute to identifying potential diagnostic biomarkers.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eA total of 356 individuals were recruited from the department of neurology at Nanjing First Hospital, Nanjing Medicial University between September 2021 and December 2024. Of these, 138 participants were excluded for not fulfilling the eligibility criteria, and 7 were excluded due to incomplete evaluations. The study ultimately comprised 127 individuals with PD and 84 healthy controls (HC). The PD group was further categorized into two subgroups: those with drooling (PD-DR, n\u0026thinsp;=\u0026thinsp;62) and those without drooling (PD-NDR, n\u0026thinsp;=\u0026thinsp;65), based on the Movement Disorder Society-Sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] part II-item 2 cutoff score of 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients initially underwent a clinical assessment by two neurologists, both of whom were specialized in movement disorders and experienced in identifying psychiatric conditions associated with PD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInclusion criteria: 1)\u0026thinsp;\u0026ge;\u0026thinsp;18 years; 2) A clinical diagnosis of idiopathic Parkinson's disease (IPD) in accordance with the MDS diagnostic criteria for PD; 3) Capable of completing all scale assessments with medical guidance. Exclusion criteria: 1)atypical or secondary parkinsonism; 2) Other neurological conditions or injuries (including traumatic brain injury, ischemic or hemorrhagic stroke, epilepsy, demyelinating disease, myopathy); 3) Unstable psychiatric disorders, such as schizophrenia or major depression or anxiety; 4) Significant cognitive deterioration, as indicated by a Mini-Mental State Examination (MMSE) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] score of less than 24; 5) Systemic diseases impacting the function of the heart, lungs, liver, kidneys, and other organs that could influence drooling; 5) Acute or chronic infection; 6) Diabetes, hypothyroidism, tumors, hematologic disorders and autoimmune diseases; 7) Inability to comply with study requirements or exhibit poor adherence.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNeuropsychological assessment\u003c/h3\u003e\n\u003cp\u003eDemographic data, including age, gender, and body mass index (BMI), were collected for all participants. The onset age and disease duration were documented for each individual with PD. Additionally, the levodopa-equivalent daily dose (LEDD) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was calculated for each patient. All PD patients underwent clinical assessments using standardized scales. Disease and motor severity were assessed in PD patients using the Hoehn and Yahr (H-Y) scale and the MDS-UPDRS part III. The effects of motor and non-motor symptoms on daily living were measured by the MDS-UPDRS parts I and II, respectively. Motor complications were assessed with the MDS-UPDRS part IV. Non-motor symptoms were quantified using the Non-Motor Symptom Scale (NMSS), considering both total and domain scores. Cognitive function was assessed using the MMSE and the Montreal Cognitive Assessment (MoCA). Anxiety and depression levels were determined using the Hamilton Anxiety Scale (HAMA) and the Hamilton Depression Scale (HAMD), respectively. Drooling was evaluated with the Drooling Clinical Scale for Parkinson's disease (SCS-PD) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All evaluations were performed with participants in the \"Off\" state, requiring participants to cease dopamine receptor agonist intake 72 hours and discontinue other anti-Parkinson's disease medications 12 hours before assessment.\u003c/p\u003e\n\u003ch3\u003eHematology Testing Approaches\u003c/h3\u003e\n\u003cp\u003eWe documented the fundamental details of each participant, including their name, gender, and age. Blood specimens were obtained from all patients at 7:30 a.m. after a 12-hour fast. For routine hematological assessments, 2 milliliters of EDTA-anticoagulated whole blood was utilized. Additionally, 5 milliliters of blood containing coagulant was employed for conventional biochemical analyses. All assays were performed utilizing commercial kits, with qualified personnel following the protocols specified by the manufacturers. The neutrophil (NEU), lymphocyte (LYM), and platelet (PLT) counts were determined using an automated SYSMEX XN series and MINDRAY BC series five-part differential blood cell analyzers manufactured in Japan. The neutrophil-to-lymphocyte ratio (NLR; NEU/LYM) and systemic inflammation index (SII; PLT\u0026times;NEU/LYM) were calculated. Furthermore, lactate dehydrogenase (LDH) serum levels were assessed utilizing the enzyme rate method on a BECKMAN COULTER AU5800 automated biochemical analyzer.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe data analyses were performed using SPSS Version 26.0. Data distribution and normality were measured using the Shapiro-Wilk test. Normality distribution data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Skewness distribution data are presented as medians with interquartile ranges. Two-tailed t-test or Mann-Whitney U test was used for comparison between the two groups. For multi-group comparison, parametric data were analyzed by One-way ANOVA post hoc multiple comparisons with Tukey correction, and non-parametric data were analyzed by Kruskal-Wallis test post hoc multiple comparisons with Bonferroni correction. The chi-square test was performed on binary variables. Univariate logistic regression analyses were used to identify risk factors associated with drooling in PD patients. After that, factors with significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included as covariates in the multivariate logistic regression analysis to further explore their relationship with drooling in PD. The effects were expressed in terms of odds ratios (OR) and their respective 95% confidence intervals (CI). A receiver operating characteristic (ROC) curve was used to estimate the sensitivity, and specificity of predictive factors for the occurrence of drooling in PD patients. Furthermore, we conducted Restricted Cubic Spline analysis (RCS) in R version 4.3.3 to investigate the correlation between the severity of drooling in PD patients and the factors and used cloud-rain plot plots to illustrate the differences among the groups. \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eClinical and demographic characteristics of PD patients and HC\u003c/h2\u003e\u003cp\u003eInitially, 356 candidate patients with PD were recruited. After exclusion of those who did not meet the inclusion criteria or had incomplete data, a total of 127 individuals with PD and 84 HC were enrolled in this study. Among the 127 PD patients, 52.8% were male and 47.2% were female, with an average age of 65.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.09 years. The median BMI was 23.38kg/m\u003csup\u003e2\u003c/sup\u003e (21.30-25.61). The median age at onset of PD was 62.31 years (54.22\u0026ndash;68.34). The median disease duration was 3.67 years (1.99\u0026ndash;6.02). The median H-Y stage was 2.00 (2.00-2.50), and the median LEDD was 300.00 mg/day (175.00-450.00). No significant differences in age and gender were observed between the PD groups and HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, PD patients exhibited a significantly lower BMI and markedly higher NLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SII (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and LDH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) levels compared to the HC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additional demographic details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicodemographic characteristics of study subjects.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePD (n\u0026thinsp;=\u0026thinsp;127)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePD-DR (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePD-NDR (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years\u003csup\u003e)a\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.88\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (M/F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67/60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39/45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34/28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33/32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.38(21.30-25.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.12(22.86\u0026ndash;26.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.35(21.18\u0026ndash;25.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.44(21.31\u0026ndash;25.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.039*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset (years)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.31(54.22\u0026ndash;68.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.38\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.87\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.040*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration (years)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.67(1.99\u0026ndash;6.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.48(2.55\u0026ndash;6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.07(1.44\u0026ndash;5.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.016*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEDD (mg/day)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300.00(175.00-450.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e312.50(200.00-481.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300.00(143.75\u0026ndash;450.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH-Y stage\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00(2.00-2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00(2.00-2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00(2.00-2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.00(34.00\u0026ndash;56.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.50(41.75\u0026ndash;66.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.00(28.00-51.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS I score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00(3.00\u0026ndash;10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.50(3.00\u0026ndash;11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00(2.50-8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS II score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.00(5.00\u0026ndash;13.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.00(8.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.00(3.00\u0026ndash;10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS III score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.00(23.00\u0026ndash;38.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.00(24.75\u0026ndash;41.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.00(17.50\u0026ndash;35.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.014*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS IV score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNMSS score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.00(13.00\u0026ndash;33.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.00(14.50\u0026ndash;42.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.00(10.00\u0026ndash;26.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNMSS-19 domain score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCS-PD score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00(0.00\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00(2.00\u0026ndash;7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00(0.00\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.00(26.00\u0026ndash;29.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.00(25.00\u0026ndash;29.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.00(26.50\u0026ndash;29.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOCA score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.00(18.00\u0026ndash;25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.50(18.00\u0026ndash;25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.00(18.50\u0026ndash;27.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.023*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAMD score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00(2.00\u0026ndash;10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.50(2.75-11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00(1.00-9.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAMA score\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00(2.00\u0026ndash;10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.00(3.00-10.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.00(2.00\u0026ndash;8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30(1.59\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64(1.35\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.47(2.02\u0026ndash;3.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.01(1.42\u0026ndash;2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.021*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e404.04(290.61-563.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e312.49(241.83-414.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e463.81(316.98-658.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e366.21(269.49-508.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171.00(156.00-191.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158.15(145.00-176.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179.00(164.75-208.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e165(141.50\u0026ndash;187.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.004**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e*Indicates significant difference; **Indicates extremely significant difference.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003eOne-way ANOVA post hoc multiple comparisons with Tukey correction.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003eKruskal-Wallis test post hoc multiple comparisons with Bonferroni correction.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ec\u003c/sup\u003eTwo-tailed t-test:PD-DR versus PD-NDR.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e1\u003c/sup\u003eTwo-tailed t-test or Chi-square test or Mann-Whitney U test:PD versus HC.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e2\u003c/sup\u003eOne-way ANOVA or Chi-square test or Kruskal-Wallis test: Comparison among PD-DR, PD-NDR and HC .\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e3\u003c/sup\u003ePost hoc multiple comparisons:PD-DR versus PD-NDR.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e4\u003c/sup\u003ePost hoc multiple comparisons: PD-DR versus HC.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e5\u003c/sup\u003ePost hoc multiple comparisons:PD-NDR versus HC.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003ePD, Parkinson's disease; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; HCs, healthy controls; BMI, Body Mass Index; LEDD, levodopa equivalent daily dose; H-Y stage, modified Hoehn-Yahr stage; MDS-UPDRS score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale total score; MDS-UPDRS I score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part I score; MDS-UPDRS II score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part II score; MDS-UPDRS III score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III score; MDS-UPDRS IV score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part IV score; NMSS score, Non-Motor Symptom Scale score; SCS-PD score, Sialorrhea Clinical Scale for Parkinson's disease score; MMSE score, Mini-Mental State Examination score; MOCA score, Montreal Cognitive Assessment score; HAMD score, Hamilton Depression Scale score; HAMA score, Hamilton Anxiety Scale score; NLR, neutrophilocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; LDH, lactate dehydrogenase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGroup comparisons between PD-DR patients, PD-NDR patients and HC\u003c/h3\u003e\n\u003cp\u003eAs stated previously, patients were divided based on MDS-UPDRS part II-item 2 into either the PD-DR group (n\u0026thinsp;=\u0026thinsp;62) or the PD-NDR group (n\u0026thinsp;=\u0026thinsp;65). The Chi-square test indicates that there were no significant differences in gender among PD-DR, PD-NDR and HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The One-way ANOVA indicates that there were significant differences in age and BMI among PD-DR, PD-NDR and HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039). The Kruskal-Wallis test revealed highly significant differences in NLR, SII, and LDH levels among the PD-DR group, PD-NDR group, and HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, post hoc analyses within the PD-DR group showed significantly higher NMSS-19 domain scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SCS-PD scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the PD-NDR group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences were observed between the PD-DR group and the PD-NDR group in terms of gender, BMI, LEDD, H-Y stage, MDS-UPDRS I score, MDS-UPDRS IV score, MMSE score, HAMD score, and HAMA score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nevertheless, the PD-DR group exhibited significantly higher values in age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), age of onset (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), disease duration (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), MDS-UPDRS total score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MDS-UPDRS II score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MDS-UPDRS III score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), NMSS score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and MoCA score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) compared to the PD-NDR group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Besides, the PD-DR group had markedly higher levels of NLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and LDH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) than the PD-NDR group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, there was no significant variation in SII between the groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePost-hoc multiple comparisons revealed that there were no statistically significant differences in age, gender, or BMI between the PD-DR group and HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while the PD-DR group manifested significantly elevated levels of NLR, SII, and LDH compared to HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Meanwhile, no significant differences were observed in age, gender, BMI, SII, and LDH levels between the PD-NDR group and HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), whereas the NLR level in the PD-NDR group was significantly higher than that in HC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eUnivariate and Multivariate Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analyses between the PD-DR and PD-NDR groups revealed that age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), age of onset (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), MDS-UPDRS III scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), MOCA scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), and LDH levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) significantly correlate with drooling in PD. Subsequently, these significant factors were incorporated as covariates into the subsequent multivariate logistic regression analysis. The findings indicated that: both in the PD-DR group and the PD-NDR group, individuals with elevated LDH levels exhibited an increased likelihood of experiencing drooling, which reached statistical significance (OR\u0026thinsp;=\u0026thinsp;1.014, 95% CI 1.000-1.027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As such means that for each unit increase in LDH levels, the odds of experiencing drooling in PD patients increased by 1.4% while holding the other variables constant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate logistic regression analysis to identify risk factors associated with drooling in PD patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePotential risk factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eBetween PD-DR and PD-NDR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.050 (1.012\u0026ndash;1.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.060 (0.948\u0026ndash;1.185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (M/F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.849 (0.423\u0026ndash;1.706)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.015 (0.918\u0026ndash;1.123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.039 (1.001\u0026ndash;1.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.966 (0.865\u0026ndash;1.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease duration (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.100 (0.992\u0026ndash;1.220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLEDD (mg/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.001 (1.000-1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH-Y stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.780 (0.995\u0026ndash;3.185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS I score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.073 (1.000-1.152)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS III score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.034 (1.005\u0026ndash;1.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.014 (0.981\u0026ndash;1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDS-UPDRS IV score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.013 (0.883\u0026ndash;1.164)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.927 (0.842\u0026ndash;1.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOCA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.934 (0.875\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.968 (0.896\u0026ndash;1.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAMD score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.027 (0.968\u0026ndash;1.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAMA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.052 (0.992\u0026ndash;1.116)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.340 (0.996\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.001 (1.000-1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.019 (1.006\u0026ndash;1.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.014 (1.000-1.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBetween PD-DR and HC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.039 (1.003\u0026ndash;1.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.011 (0.968\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.920 (0.828\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.664 (2.495\u0026ndash;8.718)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.583 (2.550-22.547)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.004 (1.002\u0026ndash;1.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.998 (0.994\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.034 (1.018\u0026ndash;1.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.032 (1.015\u0026ndash;1.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Indicates significant difference.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003ePD, Parkinson's disease; OR, Odds ratio; CI, confidence interval; BMI, Body mass index; LEDD, Levodopa equivalent daily dose; H-Y stage, modified Hoehn-Yahr stage; MDS-UPDRS I score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part I score; MDS-UPDRS III score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III score; MMSE score, Mini-Mental State Examination score; MOCA score, Montreal Cognitive Assessment score; HAMD score, Hamilton Depression Scale score; HAMA score, Hamilton Anxiety Scale score; NLR, neutrophilocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; LDH, lactate dehydrogenase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, univariate logistic regression analyses comparing the PD-DR to HC demonstrated that age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), NLR levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SII levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LDH levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly correlate with drooling in PD. Subsequently, these significant factors were incorporated as covariates into the subsequent multivariate logistic regression analysis. The findings indicated that: both in the PD-DR subgroup and the HC group, an augmented NLR value correlated with a heightened risk of drooling in PD, with the difference being statistically significant (OR\u0026thinsp;=\u0026thinsp;7.583, 95% CI 2.550-22.547, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, if NLR levels increased by 1 unit, the odds of drooling in PD patients would be 7.583 times higher, assuming other variables remain unchanged. This indicates that NLR could be a strong predictor of drooling in PD. Additionally, a higher LDH level was associated with an increased risk of drooling in PD patients, and this association was also statistically significant (OR\u0026thinsp;=\u0026thinsp;1.032, 95% CI 1.015\u0026ndash;1.049, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar to the previous comparison, this implies that for each unit increase in LDH levels, the odds of drooling increased by 3.2%.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eROC Analysis\u003c/h2\u003e\u003cp\u003eUtilizing ROC curves, a serum LDH level of 174.50mmol/L was found to differentiate between PD patients with and without drooling to a certain extent, with an AUC curve of 0.662, which translates to a sensitivity of 58.10% and a specificity of 70.80% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe AUC, 95% CI, P. value, optimal cutoff value, sensitivity, specificity, and youden index of LDH in discriminating PD-DR (n\u0026thinsp;=\u0026thinsp;62) and from PD-NDR (n\u0026thinsp;=\u0026thinsp;65).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDELncRNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptimal cutoff value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYouden index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.567\u0026ndash;0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e174.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Significant difference, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ROC, receiver operating characteristic; CI, Confidence interval; DElncRNA, differentially expressed long non-coding RNA; AUC, area under the ROC curve; PD-DR, PD patients with drooling; PD-NDR, PD patients without drooling; LDH, lactate dehydrogenase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUtilizing ROC curves, a serum NLR level of 2.05 could potentially differentiate PD patients with drooling from HC, with an area under the AUC curve of 0.785, indicating a sensitivity of 74.20% and a specificity of 77.40% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Similarly, a serum LDH level of 163.79mmol/L could distinguish PD patients with drooling from HC, with an AUC of 0.732, corresponding to a sensitivity of 79.00% and a specificity of 60.70% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, combining serum NLR levels with LDH levels could enhance the differentiation of PD patients with drooling from HC, achieving an AUC of 0.835. When the cut-off value was 0.47, it corresponds to a sensitivity of 71.00% and a specificity of 83.30% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe AUC, 95% CI, P. value, optimal cutoff value, sensitivity, specificity, and youden index of NLR, LDH and NLR combined with LDH in discriminating PD-DR (n\u0026thinsp;=\u0026thinsp;62) and from HC (n\u0026thinsp;=\u0026thinsp;84).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDELncRNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptimal cutoff value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYouden index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.709\u0026ndash;0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.650\u0026ndash;0.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u0026thinsp;+\u0026thinsp;LDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.768\u0026ndash;0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Significant difference, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ROC, receiver operating characteristic; CI, Confidence interval; DElncRNA, differentially expressed long non-coding RNA; AUC, area under the ROC curve; PD-DR, PD patients with drooling; HC, healthy control; NLR, neutrophilocyte-to-lymphocyte ratio; LDH, lactate dehydrogenase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRCS Analysis\u003c/h2\u003e\u003cp\u003eEmploying an RCS-fitted linear regression model, and adjusting for variables such as age, gender and BMI, we examined the correlation between LDH levels, NLR levels, and SCS scores within the PD-DR subgroup. The analysis revealed a significant linear association between LDH levels and SCS scores (\u003cem\u003ep\u003c/em\u003e for overall\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.077), indicating a positive correlation where an increase in LDH levels is accompanied by a significant rise in SCS scores, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. Conversely, a significant nonlinear \"U\"-shaped relationship was observed between NLR levels and SCS scores (\u003cem\u003ep\u003c/em\u003e for overall\u0026thinsp;=\u0026thinsp;0.057, \u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.048). The NLR level of the lowest point of SCS score is about 2.47, and when the NLR level is less than 2.47, the SCS score gradually decreases with the increase of NLR level. When the NLR level exceeded 2.47, the SCS score increased with the increase of the NLR level, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInflammation is increasingly recognized as a key factor in the pathological features and symptoms of PD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Preclinical and clinical studies in PD patients have shown evidence of elevated inflammatory responses in the central and peripheral systems, such as microglial activation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], as well as increased levels of immune and inflammatory markers in the brain and peripheral blood [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there is a lack of studies exploring the connection between these immune and inflammatory factors and drooling in individuals with PD. Notably, our study found that serum NLR, SII, and LDH levels were significantly elevated in PD patients. The increased serum NLR and LDH levels were associated with a heightened risk of drooling in PD, and both, as well as their combination, can serve as potential blood biomarkers for the diagnosis of PD-related drooling.\u003c/p\u003e\u003cp\u003eWithin the PD groups, the PD-DR group displayed significantly elevated NLR and LDH levels compared to the PD-NDR group, and the salivation symptoms became more severe with increasing LDH levels. Notably, we made a novel discovery that elevated LDH levels independently predict drooling in PD patients. Furthermore, through the ROC curve analysis, we also found that LDH has a significant predictive advantage over NLR and SII in forecasting the development of drooling in PD patients. This might suggest that LDH levels could serve as a new blood biomarker for drooling in PD patients and shed light on the underlying pathological mechanisms of drooling in PD. Studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]have shown that LDH inhibitors can reduce the release of pro-inflammatory cytokines (such as TNF-α and IL-6) from macrophages at inflamed sites and increase the release of the anti-inflammatory cytokine IL-10, indicating that LDH plays a significant role in regulating inflammatory responses. Additionally, LDH can regulate lymphocyte activity, and enhance T cell proliferation and cytotoxicity when exogenously added, while LDH inhibitors can block T cell activation and stabilize regulatory T cells, thereby influencing immune responses. Besides, the overexpression of hexokinase 2 and LDH-A, coupled with elevated lactate levels, contributes to the apoptosis of dopaminergic neurons in PD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The LDH, a ubiquitous enzyme found in diverse bodily tissues, plays a pivotal role in catalyzing the interconversion between pyruvate and lactate, thereby modulating the systemic levels of lactic acid. Lactic acid has been found to enhance the aggregation of α-synuclein within neurons [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], thereby triggering cell death. Consequently, it is plausible that lactate, a byproduct of LDH-catalyzed glycolysis, plays a role in the apoptotic processes affecting dopaminergic neurons in PD. Besides, LDH is implicated in energy metabolism within cells, and alterations in its levels might indicate augmented cell damage or oxidative stress in the brain. Future research should further explore the role of LDH in PD, particularly in relation to drooling, and examine its interactive dynamics with a spectrum of other biomarkers.\u003c/p\u003e\u003cp\u003eOur study demonstrated a significant nonlinear \u0026ldquo;U\u0026rdquo;-shaped curve relationship between the NLR and the severity of drooling within the PD-DR subgroup. After adjusting for confounding factors (age, gender, BMI), the analysis showed that when NLR was below 2.47, higher NLR correlated with less severe drooling; when NLR exceeded 2.47, higher NLR was linked to more severe drooling. The NLR, which integrates the counts of two key leukocyte subtypes\u0026mdash;neutrophils and lymphocytes\u0026mdash;serves as a reflective measure of the equilibrium between systemic inflammation and immune response. Neutrophils are indicative of chronic inflammatory conditions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while lymphocytes might embody the regulatory arm of the immune system [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The NLR has been suggested to be elevated in PD as a biomarker of peripheral inflammation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], as observed in our study. At present, no research exists on NLR levels in PD patients with drooling. For the first time, we observed a significant increase in NLR levels in PD patients with drooling, and it may serve as a promising noninvasive diagnostic biomarker for PD patients with drooling. However, the precise mechanism of the NLR in PD patients with drooling remains elusive and warrants further investigation through cellular studies and animal models in future research.\u003c/p\u003e\u003cp\u003eSimilar to previous studies, we also found that SII levels were elevated in PD patients compared to HC. The SII, which combines three complementary cells (neutrophils, platelets, and lymphocytes) that regulate different inflammatory or immune pathways, is an objective marker of the balance between systemic inflammation and immune response within the host. It is worth mentioning that both NLR and SII were less affected by confounding conditions and were more predictive in assessing chronic systemic inflammatory status than neutrophils, platelets, or lymphocytes alone. Over the past few years, SII has been utilized to assess the short-term adverse outcomes in acute ischemic stroke [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and to predict the prognosis of patients suffering from spontaneous cerebral hemorrhage [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, higher SII could increase the risk of mild cognitive impairment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A recent study has identified SII as a significant factor influencing the motor function of PD patients, and the findings reveal a substantial negative correlation between SII levels and scores of Activities of Daily Living [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although there was no statistically significant difference in SII between the PD-DR group and PD-NDR group, we could observe that the SII levels of PD-DR group were higher than that of PD-NDR group. We speculate that the observed results may be attributed to the sample size, and we intend to broaden our sample in future studies to delve into the variations of SII levels between the two groups.\u003c/p\u003e\u003cp\u003eHowever, this study had several limitations: (1) the sample size was modest, which might result in selection bias; (2) given its cross-sectional design, the study could not capture the longitudinal progression, and thus, further research is required to determine the potential of these markers as prognostic indicators; (3) future studies should consider integrating multi-dimensional indices, such as neuroimaging, neuro-electrophysiology and cerebrospinal fluid analysis, to achieve a more comprehensive understanding of the pathological processes and to enhance the predictive accuracy of biomarkers for PD patients with drooling; (4) this study's inflammatory indicators were limited to terminal markers in inflammatory pathways. Key molecular markers along these pathways, including cytokines, chemokines and complement pathway molecules, which could potentially yield further insights, were not measured; (5) this study did not include C-reactive protein or high-sensitivity C-reactive protein, indicators of chronic inflammation more commonly used in clinical settings. Future research should ideally incorporate these elements; (6) the underlying mechanisms of how blood markers are associated with salivation in PD, as identified in this study, are not yet understood and warrant further investigation through methods like in vitro cytological studies and experiments using animal models.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur research indicated a correlation between LDH, NLR levels and the severity of drooling in PD, and identified LDH as an independent risk factor for drooling in PD, serving as a novel peripheral blood biomarker for predicting drooling in PD patients. Monitoring the dynamic changes in peripheral blood LDH and NLR levels upon admission is a simple and cost-effective method. It not only aids in the early detection of drooling symptoms in PD patients, providing timely clues for diagnosis and prognosis, and assisting in prevention and treatment, but also contributes to the study of how immune-inflammatory responses are involved in neurodegenerative diseases. Additional studies are required to confirm these findings and to deepen our understanding of the underlying mechanisms of drooling in PD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eStudy design and manuscript draft: Ying-Dong Zhang, Ting Huang, You-Yong Tian, Xi-Xi Wang, Jia-Liang Shi; recruitment of subjects and data collection: Ting Huang, Xi-Xi Wang, Jia-Liang Shi; data analysis and discussion: Ting Huang, Xi-Xi Wang, Jia-Liang Shi, Hui-hui Jin, Qin Gao, Qing Huang, Liang Wu. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Science and Technology Innovation 2030 - Major program of \u0026quot;Brain Science and Brain-Inspired Intelligence Research\u0026quot; (Grant No.2021ZD0201807).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData generated during this study are available upon reasonable request from the corresponding author. Requests for data should be directed to [[email protected]], and will be considered in accordance with relevant data-sharing policies and ethical guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e This study was approved by the Ethics Committee of Nanjing First Hospital (No.KY20220124-06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent statement\u003c/strong\u003e All participants provided informed consent prior to their involvement in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSrivanitchapoom P, Pandey S, Hallett M (2014) Drooling in Parkinson's disease: a review. Parkinsonism Relat Disord 20:1109\u0026ndash;1118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.parkreldis.2014.08.013\u003c/span\u003e\u003cspan address=\"10.1016/j.parkreldis.2014.08.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZlotnik Y, Balash Y, Korczyn AD, Giladi N, Gurevich T (2015) Disorders of the oral cavity in Parkinson's disease and parkinsonian syndromes. Parkinsons Dis 2015:379482. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2015/379482\u003c/span\u003e\u003cspan address=\"10.1155/2015/379482\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalf JG, Smit AM, Bloem BR, Zwarts MJ, Munneke M (2007) Impact of drooling in Parkinson's disease. J Neurol 254:1227\u0026ndash;1232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00415-007-0508-9\u003c/span\u003e\u003cspan address=\"10.1007/s00415-007-0508-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eN\u0026oacute;brega AC, Rodrigues B, Torres AC, Scarpel RD, Neves CA, Melo A (2008) Is drooling secondary to a swallowing disorder in patients with Parkinson's disease? Parkinsonism Relat Disord 14:243\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.parkreldis.2007.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.parkreldis.2007.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalf JG, Munneke M, van den Engel-Hoek L, de Swart BJ, Borm GF, Bloem BR, Zwarts MJ (2011) Pathophysiology of diurnal drooling in Parkinson's disease. Mov Disord 26:1670\u0026ndash;1676. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mds.23720\u003c/span\u003e\u003cspan address=\"10.1002/mds.23720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAra\u0026uacute;jo B, Caridade-Silva R, Soares-Guedes C, Martins-Macedo J, Gomes ED, Monteiro S, Teixeira FG (2022) Neuroinflammation and Parkinson's Disease-From Neurodegeneration to Therapeutic Opportunities. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cells11182908\u003c/span\u003e\u003cspan address=\"10.3390/cells11182908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cells 11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTansey MG, Wallings RL, Houser MC, Herrick MK, Keating CE, Joers V (2022) Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol 22:657\u0026ndash;673. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41577-022-00684-6\u003c/span\u003e\u003cspan address=\"10.1038/s41577-022-00684-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalabrese V, Santoro A, Monti D, Crupi R, Di Paola R, Latteri S, Cuzzocrea S, Zappia M, Giordano J, Calabrese EJ, Franceschi C (2018) Aging and Parkinson's Disease: Inflammaging, neuroinflammation and biological remodeling as key factors in pathogenesis. Free Radic Biol Med 115:80\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.freeradbiomed.2017.10.379\u003c/span\u003e\u003cspan address=\"10.1016/j.freeradbiomed.2017.10.379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindqvist D, Hall S, Surova Y, Nielsen HM, Janelidze S, Brundin L, Hansson O (2013) Cerebrospinal fluid inflammatory markers in Parkinson's disease\u0026ndash;associations with depression, fatigue, and cognitive impairment. Brain Behav Immun 33:183\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbi.2013.07.007\u003c/span\u003e\u003cspan address=\"10.1016/j.bbi.2013.07.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang LX, Liu C, Shao YQ, Jin H, Mao CJ, Chen J (2022) Peripheral blood inflammatory cytokines are associated with rapid eye movement sleep behavior disorder in Parkinson's disease. Neurosci Lett 782:136692. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neulet.2022.136692\u003c/span\u003e\u003cspan address=\"10.1016/j.neulet.2022.136692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMangoni AA, Zinellu A (2024) The diagnostic role of the systemic inflammation index in patients with immunological diseases: a systematic review and meta-analysis. Clin Exp Med 24:27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10238-024-01294-3\u003c/span\u003e\u003cspan address=\"10.1007/s10238-024-01294-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZahorec R (2021) Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl Lek Listy 122:474\u0026ndash;488. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4149/bll_2021_078\u003c/span\u003e\u003cspan address=\"10.4149/bll_2021_078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong X, Qiang Y, Wang L, Zhang Y, Li J, Feng J, Cheng W, Tan L, Yu J (2023) Peripheral immunity and risk of incident brain disorders: a prospective cohort study of 161,968 participants. Transl Psychiatry 13:382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-023-02683-0\u003c/span\u003e\u003cspan address=\"10.1038/s41398-023-02683-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDommershuijsen LJ, Ruiter R, Erler NS, Rizopoulos D, Ikram MA, Ikram MK (2022) Peripheral Immune Cell Numbers and C-Reactive Protein in Parkinson's Disease: Results from a Population-Based Study. J Parkinsons Dis 12:667\u0026ndash;678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/jpd-212914\u003c/span\u003e\u003cspan address=\"10.3233/jpd-212914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim R, Kang N, Byun K, Park K, Jun JS (2023) Prognostic significance of peripheral neutrophils and lymphocytes in early untreated Parkinson's disease: an 8-year follow-up study. J Neurol Neurosurg Psychiatry 94:1040\u0026ndash;1046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jnnp-2022-330394\u003c/span\u003e\u003cspan address=\"10.1136/jnnp-2022-330394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLau K, Kotzur R, Richter F (2024) Blood-brain barrier alterations and their impact on Parkinson's disease pathogenesis and therapy. Transl Neurodegener 13:37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40035-024-00430-z\u003c/span\u003e\u003cspan address=\"10.1186/s40035-024-00430-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePostuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G (2015) MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30:1591\u0026ndash;1601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mds.26424\u003c/span\u003e\u003cspan address=\"10.1002/mds.26424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFolstein MF, Folstein SE, McHugh PR (1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0022-3956(75)90026-6\u003c/span\u003e\u003cspan address=\"10.1016/0022-3956(75)90026-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE (2010) Systematic review of levodopa dose equivalency reporting in Parkinson's disease. Mov Disord 25:2649\u0026ndash;2653. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mds.23429\u003c/span\u003e\u003cspan address=\"10.1002/mds.23429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerez Lloret S, Pir\u0026aacute;n Arce G, Rossi M, Caivano Nemet ML, Salsamendi P, Merello M (2007) Validation of a new scale for the evaluation of sialorrhea in patients with Parkinson's disease. Mov Disord 22:107\u0026ndash;111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/mds.21152\u003c/span\u003e\u003cspan address=\"10.1002/mds.21152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePajares M, A IR, Manda G, Bosc\u0026aacute; L, Cuadrado A (2020) Inflammation in Parkinson's Disease: Mechanisms and Therapeutic Implications. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cells9071687\u003c/span\u003e\u003cspan address=\"10.3390/cells9071687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cells 9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCerto M, Tsai CH, Pucino V, Ho PC, Mauro C (2021) Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat Rev Immunol 21:151\u0026ndash;161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41577-020-0406-2\u003c/span\u003e\u003cspan address=\"10.1038/s41577-020-0406-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManerba M, Di Ianni L, Govoni M, Roberti M, Recanatini M, Di Stefano G (2017) Lactate dehydrogenase inhibitors can reverse inflammation induced changes in colon cancer cells. Eur J Pharm Sci 96:37\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejps.2016.09.014\u003c/span\u003e\u003cspan address=\"10.1016/j.ejps.2016.09.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Chen L, Qin Q, Wang D, Zhao J, Gao H, Yuan X, Zhang J, Zou Y, Mao Z, Xiong Y, Min Z, Yan M, Wang CY, Xue Z (2022) Upregulated hexokinase 2 expression induces the apoptosis of dopaminergic neurons by promoting lactate production in Parkinson's disease. Neurobiol Dis 163:105605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbd.2021.105605\u003c/span\u003e\u003cspan address=\"10.1016/j.nbd.2021.105605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang P, Gan M, Ebrahim AS, Castanedes-Casey M, Dickson DW, Yen SH (2013) Adenosine monophosphate-activated protein kinase overactivation leads to accumulation of α-synuclein oligomers and decrease of neurites. Neurobiol Aging 34:1504\u0026ndash;1515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neurobiolaging.2012.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.neurobiolaging.2012.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoehnlein O, Steffens S, Hidalgo A, Weber C (2017) Neutrophils as protagonists and targets in chronic inflammation. Nat Rev Immunol 17:248\u0026ndash;261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nri.2017.10\u003c/span\u003e\u003cspan address=\"10.1038/nri.2017.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTansey MG, Romero-Ramos M (2019) Immune system responses in Parkinson's disease: Early and dynamic. Eur J Neurosci 49:364\u0026ndash;383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ejn.14290\u003c/span\u003e\u003cspan address=\"10.1111/ejn.14290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosseini S, Shafiabadi N, Khanzadeh M, Ghaedi A, Ghorbanzadeh R, Azarhomayoun A, Bazrgar A, Pezeshki J, Bazrafshan H, Khanzadeh S (2023) Neutrophil to lymphocyte ratio in parkinson's disease: a systematic review and meta-analysis. BMC Neurol 23:333. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12883-023-03380-7\u003c/span\u003e\u003cspan address=\"10.1186/s12883-023-03380-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou YX, Li WC, Xia SH, Xiang T, Tang C, Luo JL, Lin MJ, Xia XW, Wang WB (2022) Predictive Value of the Systemic Immune Inflammation Index for Adverse Outcomes in Patients With Acute Ischemic Stroke. Front Neurol 13:836595. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2022.836595\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2022.836595\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrifan G, Testai FD (2020) Systemic Immune-Inflammation (SII) index predicts poor outcome after spontaneous supratentorial intracerebral hemorrhage. J Stroke Cerebrovasc Dis 29:105057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105057\u003c/span\u003e\u003cspan address=\"10.1016/j.jstrokecerebrovasdis.2020.105057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Li T, Li H, Li D, Wang X, Zhao A, Liang W, Xiao R, Xi Y (2022) Association of Dietary Inflammatory Potential with Blood Inflammation: The Prospective Markers on Mild Cognitive Impairment. Nutrients 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14122417\u003c/span\u003e\u003cspan address=\"10.3390/nu14122417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Zhang Q, Gao Y, Nie K, Liang Y, Zhang Y, Wang L (2021) Serum Folate, Vitamin B12 Levels, and Systemic Immune-Inflammation Index Correlate With Motor Performance in Parkinson's Disease: A Cross-Sectional Study. Front Neurol 12:665075. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2021.665075\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2021.665075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, drooling, inflammation, lactate dehydrogenase, neutrophil-to-lymphocyte ratio","lastPublishedDoi":"10.21203/rs.3.rs-7311117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eDrooling, a prevalent non-motor symptom in Parkinson\u0026rsquo;s disease (PD), lacks standardized diagnostic biomarkers. This study investigated the association between systemic inflammatory markers\u0026mdash;serum neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII) and serum lactate dehydrogenase (LDH)\u0026mdash;and drooling in PD.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e\u003cp\u003eA total of 127 PD patients and 84 healthy controls (HC) were recruited. PD patients were divided into drooling (PD-DR, n\u0026thinsp;=\u0026thinsp;62) and non-drooling (PD-NDR, n\u0026thinsp;=\u0026thinsp;65) groups. Hematological parameters, clinical scales, and neuropsychological assessments were analyzed. Statistical analysis was performed to compare clinical characteristics, logistic regression for risk factors, receiver operating characteristic (ROC) curves for diagnostic accuracy, and restricted cubic splines (RCS) for nonlinear associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe PD-DR group manifested significantly elevated levels of NLR, SII, and LDH compared to HC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, PD-DR patients also exhibited significantly elevated NLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and LDH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) levels when compared to PD-NDR patients. Multivariate logistic regression identified LDH levels (OR\u0026thinsp;=\u0026thinsp;1.014, 95% CI 1.000-1.027, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) as independent risk factors for drooling in PD patients. ROC analysis indicated that a serum LDH level of 174.50 mmol/L could differentiate between PD patients with and without drooling with an AUC of 0.662, sensitivity of 58.10%, and specificity of 70.80%. RCS analysis revealed a linear relationship between LDH and drooling severity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and a nonlinear \"U\"-shaped association for NLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study identifies serum LDH and NLR as novel biomarkers correlating with drooling severity in PD, highlighting LDH as an independent risk factor.\u003c/p\u003e\u003ch2\u003eClinical relevance:\u003c/h2\u003e\u003cp\u003eMonitoring LDH/NLR dynamics offers a cost-effective strategy for early detection and management of drooling severity in Parkinson's disease.\u003c/p\u003e","manuscriptTitle":"Serum LDH and NLR as Diagnostic Biomarkers for Drooling in Parkinson's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:52:03","doi":"10.21203/rs.3.rs-7311117/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":"f29621c2-97cc-4967-b777-c43ee3cb444d","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-16T14:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 05:52:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7311117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311117","identity":"rs-7311117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}