The roles of total cholesterol, the neutrophil-to- high-density-lipoprotein ratio and the lymphocyte-to-high-density-lipoprotein ratio in the diagnosis and progression of Parkinson's disease

The roles of total cholesterol, the neutrophil-to- high-density-lipoprotein ratio and the lymphocyte-to-high-density-lipoprotein ratio in the diagnosis and progression of 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 The roles of total cholesterol, the neutrophil-to- high-density-lipoprotein ratio and the lymphocyte-to-high-density-lipoprotein ratio in the diagnosis and progression of Parkinson's disease Yangping Tong, Bo Li, Jue Hu, Wei Xu, Fangyi Li, Liang Liu, Sufen Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4524554/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Parkinson’s disease (PD), is the second most prevalent neurodegenerative disease after Alzheimer’s disease. Inflammation, immunity dysregulation and other pathological processes play pivotal roles in the pathogenesis and progression of PD. Nevertheless, the role of peripheral inflammatory markers in diagnosing and monitoring the progression of PD remains to be fully elucidated. Patients and Methods 192 patients with PD were selected, and sex- and age-matched healthy individuals (n = 190) were included in the control group. Then, the persons’ basic information was collected, such as gender, age, smoking, blood lipids, and so on. Then, the neutrophil-to-high-density-lipoprotein ratio (NHR) and the lymphocyte -to-high-density-lipoprotein ratio (LHR), etc. were calculated. Then, the indicators were contrasted in the two groups, and, univariate and multivariate logistic regression were conducted. Pearson and Spearman correlation analyses were utilized to determine the correlation between total cholesterol (TC), the NHR, the LHR, the neutrophil-to-lymphocyte ratio (NLR), the lymphocyte-to-monocyte ratio (LMR), and the progression of PD. The nomogram was drawn using R language. Results TC, triglyceride (TG), neutrophils, monocytes, lymphocytes, serum albumin, the high-to-low-density-lipoprotein ratio (HLR), the monocyte-to-high-density-lipoprotein ratio (MHR), the NHR and the LHR in the PD group were significantly different from those in the control group ( p < 0.05). Univariate and multivariate logistic regression analyses showed that TC, the LHR, and the NHR were independent influencing factors for PD. Pearson and Spearman correlation analyses indicated a negative correlation between TC, the NHR, the LHR and the UPDRS scores in patients with PD. And it showed a negative correlation between the LHR and the Hoehn and Yahr (H&Y) staging system. Moreover, it showed significant correlations between the LHR, the NLR, the LMR, and the duration of patients with PD. Furthermore, an accuracy model of the nomogram was structured for the indicators of PD, which showed adequately sensitivity and specificity using receiver operating characteristic (ROC) curve to evaluate the diagnostic performance of TC, the LHR, and the NHR for PD. Conclusions The findings indicate that lower levels of TC, the NHR, and the LHR may be relevant for diagnosing and assessing the progression of PD, and they appear to be candidate biomarkers for PD. Parkinson’s disease inflammatory markers blood lipids diagnosis progression Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson’s disease (PD) is a neurodegenerative disease that is second after the most prevalent neurodegenerative disease, Alzheimer's disease. The global prevalence of PD increases with age and challenged in diagnosis of the disease. PD is also a kind of progressive neurological disease that currently lacks a cure with no treatments available that can effectively halt or prevent the disease's progression [ 1 – 3 ]. Aggregation of misfolded α-synuclein (α-SYN) is a characteristic pathological feature that leads to the formation of Lewy bodies. This process is followed by a significant loss of dopaminergic neurons in the substantia nigra pars compacta, a region of the midbrain. Although the exact mechanisms underlying the pathogenesis of PD are not yet completely understood, it is widely accepted that various pathological processes, including inflammation, immunity system dysregulation, oxidative stress and others, are likely to play crucial roles in both the development and progression of the disease [ 4 – 6 ]. At present, an extensive body of research indicates that inflammatory markers and the peripheral immune system are integral to the pathogenesis and progression of PD [ 7 , 8 ]. It is found that inflammation can induce or exacerbate the symptoms of PD. Microglia cells (MGs) play important roles in secreting proinflammatory mediators, and their activation can promote inflammation [ 9 ]. This process could be overactivated by α-SYN aggregation, leading to a greater inflammatory response [ 10 ]. A growing number of studies have found that the integrity and function of the BBB are often destroyed during aging or in neurodegenerative diseases [ 11 ]. Due to the impairment of the BBB in PD patients, peripheral inflammation spreads and leads to central inflammation, and the progression of PD can be delayed by inhibiting the proinflammatory T-cell response [ 12 ]. In studies on animal models and PD patients, it was found that the infiltration of T cells in peripheral immunity and inflammatory factors increased in serum as well as the activation of glial cells and proinflammatory factors raised in the brain [ 13 ]. In recent years, several novel plasma markers have been identified, including the monocyte-to-high-density lipoprotein ratio (MHR) [ 14 , 15 ], the neutrophil-to-lymphocyte ratio (NLR) [ 16 , 17 ], the lymphocyte-to-monocyte ratio (LMR) [ 18 ], the neutrophil-to-monocyte ratio (NMR) [ 19 , 20 ], and the lymphocyte-to-high-density lipoprotein ratio (LHR) [ 21 , 22 ]. These inflammatory biomarkers are pivotal in systemic inflammatory diseases, autoimmune disorders, metabolic syndrome, and oncology. They offer reliable stability for assessing systemic inflammation. Studies have shown that the NLR is closely associated with PD [ 23 , 24 ]; however, there is a scarcity of research on the neutrophil-to-high-density lipoprotein ratio (NHR) and the LHR in the relation to the onset and progression of PD. Consequently, this study aims to evaluate the correlation between these markers and PD, as well as their potential roles in the disease’s diagnosis and progression. The investigation of plasma inflammatory markers in PD not only provides a novel approach to understanding the pathophysiological mechanisms of PD but also holds significant implications for its diagnosis and treatment. Materials and methods Subjects The research protocol was approved by the Ethics Committee of Changsha Central Hospital, Hengyang Medical College, University of South China. According to the Declaration of Helsinki and the guidelines of the institute, all patients and their families signed informed consent forms and agreed to use their blood samples in the study. Inclusion criteria were as follows: (1) the clinical and laboratory examination data were complete; (2) meeting the diagnostic criteria of the International Association of Parkinson's Disease and Movement Disorders Association. The exclusion criteria were as follows: (1) other neurodegenerative diseases, such as frontotemporal dementia and Alzheimer's disease; (2) infection; (3) severe liver and kidney injury; (4) history of stroke or head trauma; (5) complications, including diabetes, hypothyroidism, tumors and blood immune system diseases; and (6) long-term use of lipid-lowering drugs before onset. The study was performed on 192 patients with PD who were hospitalized in the Department of Neurology during the period from Jan 2020 to Aug 2023. Participants of healthy controls (HCs) were selected from the Health Examination Center at the Changsha Central Hospital, Hengyang Medical College, University of South China. The same exclusion criteria were applied to ensure comparability. Methods of blood analyses and clinical evaluation Basic information was collected, such as sex, age, BMI, smoking, drinking, hypertension, etc. Each patient diagnosed with PD underwent a comprehensive set of standardized assessments, which included the following measures: the Unified Parkinson’s Disease Rating Scale (UPDRS), which was comprised of three distinct subscales: UPDRS-I for assessing the patients’ psychological well-being, UPDRS-II for examining their performance in daily activities, and UPDRS-III for evaluating motor capabilities. Additionally, the Hoehn and Yahr (H&Y) staging system was employed to gauge the clinical severity and monitor the progression of the disease in these patients. All subjects underwent blood sampling on the day they were admitted to the hospital. Blood tests included various components, including neutrophils, lymphocytes, monocytes, uric acid (UA), serum albumin, and blood lipids: triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL), etc. Subsequently, the following ratios were calculated: the NMR, the MHR, the LMR, the LHR, the NLR, the NHR and the HDL to LDL ratio (HLR). Statistical analyses Statistical analyses were performed utilizing SPSS 22.0 software (Chicago, USA). The normal distributions of measurement data were calculated by the Shapiro-Wilk test, and the results were expressed as mean ± standard deviation (mean ± SD). The analyses between the control group and PD group were used by t-test or Wilcoxon rank-sum test. And enumeration data were analyzed by chi-square test or Fisher’s exact test. In order to study the influencing factors of PD, univariate logistic regression was conducted firstly, followed by multivariate logistic regression. The results showed that statistically significant variables were as the independent influencing factors for PD (P < 0.05). Spearman or Pearson correlation analyses were performed to investigate the relationships between plasma markers and the progression of PD. After conducting a multivariate logistic regression analysis to identify independent factors, a receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic value of these factors in predicting PD. Subsequently, a nomogram was constructed using the R programming language and the "rms" package. The accuracy of the nomogram was subsequently assessed using a bootstrapped-concordance index. Results The demographic and clinical characteristics, along with laboratory features, of the control group and the PD group The demographic and clinical characteristics of both control individuals and patients are thoroughly detailed in Table 1 . The study included 92 male (47.9%) and 100 female (52.1%) patients, aged between 34 and 78 years, with average age of 65.1 ± 8.9 years. In the control group, there were 91 males (50.6%) and 99 females (49.4%), with ages ranging from 33 to 79 years and an average age of 66.1 ± 8.3 years. Patients with PD had an average body mass index (BMI) of 24.44 ± 1.64 kg/m 2 , while HCs had an average BMI of 24.16 ± 1.62 kg/m 2 . There were no significant differences in age ( p = 0.256) or BMI ( p = 0.087) between patients with PD and HCs. There were also no significant differences in sex ( p = 0.611), smoking ( p = 0.880) or drinking ( p = 0.799). The average H&Y staging score for patients with PD was 2.61 ± 0.96. The mean UPDRS Part I score was 3.55 ± 1.14, the mean UPDRS Part II score was 14.59 ± 3.09, and the mean UPDRS Part III score was 25.07 ± 7.12. And the mean duration of patients with PD was 53.66 ± 49.40 months. Table 1 Characteristics of healthy controls and PD patients Factors Controls (n = 190) PD patients (n = 192) t/Z P Age (years) 66.13 ± 8.28 65.13 ± 8.91 1.137 0.256 Sex (male, %) 91 (50.6%) 92(47.9%) .259 0.611 Smoking (male, %) 41 (22.8%) 45(23.4%) .023 0.880 Drinking (male, %) 31 (17.2%) 35 (18.2%) 0.065 0.799 BMI (kg/m 2 ) 24.44 ± 1.64 24.16 ± 1.62 0.009 0.087 Duration (months) 53.66 ± 49.40 H&Y 2.61 ± 0.96 UPDRS 43.21 ± 11.35 UPDRS I 3.55 ± 1.14 UPDRS II 14.59 ± 3.09 UPDRS III 25.07 ± 7.12 Serum albumin (g/L) 39.02 ± 3.43 39.78 ± 3.11 -2.257 0.025 UA (µmol/L) 294.48 ± 82.39 279.77 ± 84.52 1.721 0.086 TC (mmol/L) 4.46 ± 0.96 4.06 ± 0.93 4.119 0.000 TG (mmol/L) 1.51 ± 1.05 1.20 ± 0.63 3.591 0.000 HDL (mmol/L) 1.21 ± 0.26 1.26 ± 0.35 -1.570 0.117 LDL (mmol/L) 2.61 ± 0.77 2.26 ± 0.72 4.599 0.000 Neutrophils (×10 9 /L) 3.97 ± 1.73 3.38 ± 1.26 3.785 0.000 Lymphocytes (×10 9 /L) 1.73 ± 0.57 1.49 ± 0.56 4.201 0.000 Monocytes (×10 9 /L) 0.40 ± 0.12 0.34 ± 0.12 4.480 0.000 HLR 0.51 ± 0.21 0.61 ± 0.27 -4.106 0.000 NMR 10.55 ± 5.16 10.21 ± 3.85 0.742 0.459 LMR 4.57 ± 1.49 4.55 ± 1.83 0.128 0.898 NLR 2.66 ± 2.02 2.67 ± 1.59 -0.049 0.961 NHR (×10 9 /mmol) 3.40 ± 1.51 2.88 ± 1.36 3.525 0.000 LHR (×10 9 /mmol) 1.50 ± 0.63 1.25 ± 0.57 3.913 0.000 MHR (×10 9 /mmol) 0.34 ± 0.13 0.29 ± 0.14 3.684 0.000 Abbreviations: PD Parkinson’s disease, BMI: body mass index; UA uric acid, TC total cholesterol, TG triglyceride, HDL high-density lipoprotein cholesterol, LDL low-density lipoprotein cholesterol, HLR HDL to LDL ratio, NMR neutrophil to monocyte ratio, LMR lymphocyte to monocyte ratio, NLR neutrophil to lymphocyte ratio, NHR neutrophil to HDL ratio, LHR lymphocyte to HDL ratio, MHR monocyte to HDL ratio, NHR neutrophil to HDL ratio Comparative analysis of plasma markers in HCs and patients with PD To learn the different factors between the PD group and HC group, the indicators were contrasted. Substantial differences were observed in plasma serum albumin, TC, TG, LDL, neutrophil counts, lymphocyte counts, monocyte counts, the HLR, the NHR, the LHR and the MHR between patients with PD and HCs ( p 0.05, Table 1 ). Subsequently, univariate logistic regression analysis revealed significant individual factors associated with PD, which were consistent with the substantial differences in these factors observed between the two groups. Moreover, a multivariate logistic regression analysis identified TC, the NHR and the LHR as independent influencing factors for PD. These factors, with TC ( P = 0.006, OR = 0.602 < 1), the NHR ( P = 0.014, OR = 0.731 < 1), and the LHR ( P = 0.029, OR = 0.505 < 1), were significantly lower in PD patients compared to controls and were recognized as protective factors against PD, as detailed in Table 2 . Table 2 Univariate and multivariate logistic analyses of indicators for PD Factors B Crude OR (95% CI) P B Crude OR (95% CI) P TC -0.450 0.637 (0.509 ~ 0.797) 0.000 -0.472 0.624 (0.495 ~ 0.787) 0.000 LDL -0.637 0.529 (0.397 ~ 0.704) 0.000 -0.331 0.718 (0.295 ~ 1.748) 0.466 TG -0.492 0. 612 (0. 459 ~ 0.815) 0.000 -0.165 0.848 (0.607 ~ 1.185) 0.335 Serum albumin 0.071 1.074 (1.009 ~ 1.142) 0.025 0.063 1.065 (0.995 ~ 1.141) 0.071 HLR 1.777 5.910 (2.423 ~ 14.416) 0.000 -0.984 0.374 (0.063 ~ 2.199) 0.276 NHR -0.254 0.776 (0.670 ~ 0.898) 0.001 -0.216 0.806 (0.690 ~ 0.941) 0.006 LHR -0.680 0.507 (0.355 ~ 0.723) 0.000 -0.521 0.594 (0.405 ~ 0.871) 0.008 MHR -2.903 0.055 (0.011 ~ 0.272) 0.000 -0.924 0.397 (0.032 ~ 4.942) 0.473 The correlation between factors and stages of PD Plasma TC levels were negatively correlated with the UPDRS total score (r = -0.231, p < 0.05), UPDRS-I score (r = -0.214, p < 0.05), and UPDRS-III score (r = -0.191, p < 0.05, Table 3 ). Additionally, the plasma NHR and LHR were also negatively correlated with the UPDRS total score (r = -0.357 and − 0.501, respectively, p < 0.05), UPDRS-II score (r = -0.268 and − 0.365, respectively, p < 0.05, Table 3 ) and UPDRS-III score (r = -0.332 and − 0.493, respectively, p < 0.05, Table 3 ). Notably, only the plasma LHR showed a negative correlation with the H&Y score (r = -0.198, p < 0.05, Table 3 ). Interestingly, the plasma NHR and LMR were negatively correlated with the disease duration in patients with PD (r = -0.249 and − 0.186, respectively, p < 0.05). In contrast, the plasma NLR was positively correlated with the duration of PD (r = 0.195, p < 0.05, Table 3 ). Table 3 Analyzing the correlation between variables and the progression of Parkinson's disease Processes UPDRS I UPDRS II UPDRS III UPDRS H&Y Duration TC r -0.214 -0.105 -0.191 -0.231 -0.008 -0.140 p 0.003 0.149 0.008 0.001 0.907 0.052 NHR r -0.064 -0.268 -0.332 -0.357 -0.056 0.030 p 0.377 0.000 0.000 0.000 0.441 0.676 LHR r -0.036 -0.365 -0.493 -0.501 -0.198 -0.249 p 0.616 0.000 0.000 0.000 0.006 0.000 NLR r 0.007 0.065 0.087 0.078 0.104 0.195 p 0.925 0.373 0.229 0.281 0.153 0.007 LMR r -0.026 -0.072 -0.133 -0.131 -0.069 -0.186 p 0.722 0.323 0.067 0.072 0.343 0.010 Analysis of the nomogram and receiver operating characteristic (ROC) curve After univariate logistic regression analysis, multivariate analysis revealed that TC, NHR and LHR were statistically significant ( p < 0.05). The model’s effectiveness in identifying patients at high risk for developing PD was supported by an area under the curve (AUC) of 0.682 (95% CI: 0.629 to 0.735) in the ROC curve, with a sensitivity of 72.9% and a specificity of 57.4% (Fig. 3 A). Subsequently, a nomogram chart was constructed using R language and the “rms” package. The top section of the chart assigns points to each factor, including TC, NHR, and LHR. Directly beneath the total points, the chart displays the corresponding estimated probability percentage of developing PD (Fig. 2 ). The nomogram demonstrated a bootstrap-corrected concordance index of 0.682, with a strong correlation between the actual and predicted probabilities, indicating its excellent discriminative ability, and it was well calibrated with observed outcomes (Fig. 3 B). Discussion In this study, we collected data from individuals with PD and compared it with a group of HCs matched by age and sex. Notably, significant differences in several biomarkers were observed between the PD patients and the control group. We then utilized logistic regression, correlation analysis and nomogram analysis to investigate factors associated with PD, identifying inflammatory markers and blood lipid profiles as key contributors to the disease. Our thorough examination highlighted the significance of TC, NHR, and LHR as pivotal factors for diagnosing and tracking PD progression. Blood lipids, encompassing TC, TG, LDL and HDL, are integral to the pathogenesis of neurodegenerative diseases, as evidenced by substantial research [ 25 , 26 ]. Disruptions in lipid metabolism can lead to an overproduction of free radicals, which in turn compromise the body's antioxidant defenses. This results in heightened oxidative stress, causing alterations in blood rheology, thickening of the microvascular walls, and the development of vitreous lesions. These changes can culminate in vascular occlusion, leading to cerebral ischemia, hypoxia, and neurodegeneration [ 27 ]. Moreover, dyslipidemia is recognized as a potential harbinger of motor symptoms in patients with PD [ 28 ]. Existing literature on the link between blood lipids and PD yields mixed results. While Lu et al. [ 27 ] reported no significant differences in TC, TG, and LDL levels between PD patients and healthy controls, Saedi et al. [ 29 ] observed significantly reduced serum concentrations of these lipids in PD patients, including TG, LDL and TC. There is a hypothesis that inadequate or dysfunctional HDL within the brain contributes to neurodegenerative pathology [ 26 , 30 ]. In the current study, multivariate regression analysis identified TC was a significant predictor in the diagnosis of PD. We found that patients diagnosed with PD had lower TC levels than those in the control group, consist with the results from Saedi’s research [ 29 ]. Furthermore, a significant correlation was observed between TC levels and the severity of PD as assessed by the UPDRS scores. This correlation indicates that TC may be a potential biomarker for the progression of PD. Therefore, blood lipids have important roles in the progression and pathogenesis of PD. Recent studies have firmly linked inflammation with the pathogenesis of PD [ 31 , 32 ]. The LHR is a novel indicator of inflammation, which extends to metabolic syndrome (METS) and chronic obstructive pulmonary disease, where it is an independent risk factor [ 22 ]. METS has been linked to increased leukocyte and lymphocyte counts, suggesting LHR’s importance in assessing its presence and severity [ 33 ]. Studies have also shown that both the LHR and the NHR are predictive of METS in women, irrespective of other risk factors [ 34 ]. In the context of PD, lymphocyte counts are reduced, particularly CD4 + T cells, CD19 + B cells, and Treg cells [ 35 ]. Analysis of 123 newly diagnosed PD patients showed an inverse relationship between lymphocyte and neutrophil percentages and the motor score on the UPDRS [ 36 ]. While a cohort study indicated that higher lymphocyte counts are tied to a lower PD risk, it was acknowledged to have confounding factors [ 37 ]. However, the indicator LHR in PD research has not yet been investigated. In this study, through multivariate logistic regression analysis, we found that PD patients had lower LHR than controls, positing it as a protective factor. Correlation analyses, including Pearson and Spearman, demonstrated significant negative correlations between LHR and UPDRS scores, the H&Y staging system, and disease duration in PD patients. Collectively, these findings suggest that the LHR could serve as a potential biomarker, aiding in the diagnosis of PD and potentially predicting the progression of the disease. A previous study found that NHR could predict METS in women [ 34 ]. NHR is also a biomarker for nascent METS [ 38 ]. In a comparison study about acute myocardial infarction, NHR had a superior prognostic value in elderly patients [ 39 ]. However, there are few studies about NHR in PD. In this study, we discovered that the NHR was significantly lower in patients with PD compared to HCs. Multivariate logistic regression analysis further supported NHR as a protective factor. Additionally, Spearman correlation analyses revealed significant negative correlations between NHR and certain components of the UPDRS scores. These findings suggest that the NHR could serve as a potential biomarker for predicting the progression of PD Many studies have shown that the MHR is closely related to the occurrence, progress and prognosis of cardiovascular, immune system diseases and rheumatic diseases [ 14 , 15 ]. In this study, when we compared the two groups, we found that the MHR acted as a protective factor against PD, with significantly lower levels observed in patients diagnosed with PD. However, MHR did not emerge as an independent factor that could influence the diagnosis of the disease. Additionally, no significant differences were found in UA levels, the LMR and NLR between PD patients and HCs. However, both the NLR and LMR demonstrated significant correlations with disease duration in PD patients, indicating a potential link that warrants further investigation to understand the underlying mechanisms. In summary, our study delineated the roles and clinical relevance of inflammatory markers and lipid profiles in the diagnosis and progression of PD. Specifically, the LHR and NHR emerged as novel biomarkers with potential implications for both diagnosing PD and tracking its progression. Limitations Nevertheless, this study has certain limitations. Firstly, it was conducted as a single-center pilot study, which may affect the generalizability of the findings. Secondly, the research did not delve into the cellular and molecular mechanisms that could explain how inflammation impacts the development and course of PD, an area that requires further exploration. Conclusions In summary, our research has identified the plasma biomarkers TC, NHR, and LHR as key indicators in the diagnostic process and in monitoring the progression of PD. The ease of deriving these markers from standard blood tests renders them readily available for clinical application, which may improve the precision of diagnosis. However, further investigation is necessary to establish a clear link between these biomarkers and the underlying mechanisms of PD. This understanding could pave the way for more targeted and effective treatment strategies. Declarations Authors’ contributions YPT and SFC designed the study, and YPT and BL performed the statistical analyses, interpreted the results, and wrote the manuscript. WX, JH, FYL, LL participated in collecting and analysing the data, resolving difficulties in the analytic strategies and discussing the results. Finally, YPT and SFC were the final reviewers and acted as the corresponding authors. All of the authors have read as well as approved the final manuscript. Funding statement This study was supported by Hunan Provincial Health Commission foundation (20201928; to YPT), the Changsha Bureau of Science and Technology (kzd2001077; to YPT). Availability of data and materials The datasets employed and/or analyzed within the current research can be accessed from the corresponding authors upon reasonable requests. Acknowledgements We wish to show our gratitude to all those who were involved in this study. Ethics approval and consent to participate Any field of the research involved within this manuscript which covered patients of human was performed after obtaining ethics approval from all related bodies . Consent for publication All participants as well as authors have given their consent for the publication of this paper in Lipids in Health and Disease. Conflict of Interest The authors declare that they have no competing interests. References van Munster M, Stumpel J, Thieken F, et al. Moving towards Integrated and Personalized Care in Parkinson's Disease: A Framework Proposal for Training Parkinson Nurses. J Pers Med. 2021;11(7):623. Yu CC, Chen HL, Chen MH et al. (2020) Vascular Inflammation Is a Risk Factor Associated with Brain Atrophy and Disease Severity in Parkinson's Disease: A Case-Control Study. Oxid Med Cell Longev 2020: 2591248. Collaborators GBDN. 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Chen T, Chen H, Xiao H, et al. Comparison of the Value of Neutrophil to High-Density Lipoprotein Cholesterol Ratio and Lymphocyte to High-Density Lipoprotein Cholesterol Ratio for Predicting Metabolic Syndrome Among a Population in the Southern Coast of China. Diabetes Metab Syndr Obes. 2020;13:597–605. Jensen MP, Jacobs BM, Dobson R, et al. Lower Lymphocyte Count is Associated With Increased Risk of Parkinson's Disease. Ann Neurol. 2021;89(4):803–12. Umehara T, Oka H, Nakahara A, Matsuno HMurakami H. Differential leukocyte count is associated with clinical phenotype in Parkinson's disease. J Neurol Sci. 2020;409:116638. Yazdani S, Mariosa D, Hammar N, et al. Peripheral immune biomarkers and neurodegenerative diseases: A prospective cohort study with 20 years of follow-up. Ann Neurol. 2019;86(6):913–26. Jialal I, Jialal G, Adams-Huet BRamakrishnan N. (2020) Neutrophil and monocyte ratios to high-density lipoprotein-cholesterol and adiponectin as biomarkers of nascent metabolic syndrome. Horm Mol Biol Clin Investig 41(2). Huang JB, Chen YS, Ji HY, et al. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids Health Dis. 2020;19(1):59. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4524554","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310902553,"identity":"0646bab8-1dd5-440e-ba47-7d2a35cd1d2d","order_by":0,"name":"Yangping Tong","email":"","orcid":"","institution":"The Affiliated Changsha Central Hospital, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yangping","middleName":"","lastName":"Tong","suffix":""},{"id":310902554,"identity":"0b86e4e4-7559-4e42-ac47-4bf593a10cd6","order_by":1,"name":"Bo Li","email":"","orcid":"","institution":"Fuyang Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Li","suffix":""},{"id":310902555,"identity":"ef4bc88c-8491-4221-a810-5df4b55d0abe","order_by":2,"name":"Jue Hu","email":"","orcid":"","institution":"The Affiliated Changsha Central Hospital, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Jue","middleName":"","lastName":"Hu","suffix":""},{"id":310902556,"identity":"3d92ef99-e613-4c8d-af52-4d022b047301","order_by":3,"name":"Wei Xu","email":"","orcid":"","institution":"The Affiliated Changsha Central Hospital, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xu","suffix":""},{"id":310902557,"identity":"62e52015-c7c1-47ff-8841-aa24b09ea2d5","order_by":4,"name":"Fangyi Li","email":"","orcid":"","institution":"The Affiliated Changsha Central Hospital, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Fangyi","middleName":"","lastName":"Li","suffix":""},{"id":310902558,"identity":"130e0568-e530-40ec-acd1-5f17d7116f5e","order_by":5,"name":"Liang Liu","email":"","orcid":"","institution":"The Affiliated Changsha Central Hospital, University of South China","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Liu","suffix":""},{"id":310902559,"identity":"4a59b06d-0d7f-4b18-ada9-35d0b8429776","order_by":6,"name":"Sufen Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCQaGD0DMwMbAfODAhwritDDOgGhhSzw44wyxWiAsHuPDvC1E6OCf3f6wwaKizp6PvefDAd4GBnl+sQMELLlzxrBB4szhxDaesxsOSO5gMJw5OwG/FgOJHPYHkm0HEtgkcjccMDzDkGBwm6CW9IcNkv/q7Nnk3zw4kNhGlJYEwwbJBmbGNgkehgMHidEicSMH6JdjIL+kGRxsOCNB2C/8M9IfNkvU1NnLtx9+/PlPhY08vzQBLSDALIFkK2HlIMD4gTh1o2AUjIJRMFIBAEFwRuahpLmrAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Changsha Central Hospital, University of South China","correspondingAuthor":true,"prefix":"","firstName":"Sufen","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-06-04 02:09:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4524554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4524554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59052586,"identity":"3d58a620-b1b7-461f-b1c4-52976f95057e","added_by":"auto","created_at":"2024-06-25 20:17:24","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":199483,"visible":true,"origin":"","legend":"\u003cp\u003ePlasma markers were compared between PD and HCs. Significant differences in several biomarkers were observed between the PD patients and the control group. HCs healthy controls, TC total cholesterol, TG triglyceride, HDL high-density lipoprotein cholesterol, LDL low-density lipoprotein cholesterol, HLR HDL to LDL ratio, NHR neutrophil-to-HDL ratio, LHR lymphocyte-to-HDL ratio, MHR monocyte-to-HDL ratio, NHR neutrophil-to-HDL ratio. *\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4524554/v1/bbe41c0607a7084cbf0c7a04.jpeg"},{"id":59052584,"identity":"298bd47d-0b74-4add-ba9a-ed228f014d04","added_by":"auto","created_at":"2024-06-25 20:17:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65380,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for blood lipids and inflammatory markers in PD. To estimate the probability of PD, we marked the patients’ indicator values at each axis and drew a straight line to obtain the points. Then, we summed the total points for all variables and drew a straight line to obtain the probability.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4524554/v1/4528ac06a2d94c6c85a4d1de.jpeg"},{"id":59053028,"identity":"bbc6eb1a-2e4c-45d6-a038-3d6439f7fc94","added_by":"auto","created_at":"2024-06-25 20:25:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86951,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the prediction model and the calibration of the nomogram for PD. A. The area under the curve was 0.682 (95% CI: 0.629 ~ 0.735). B. The x-axis represents the predicted probability of PD, and the y-axis represents the observed probability of PD. It shows a great related line between the observed and predicted probability.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4524554/v1/d3453963527d2dbebcc59c7a.jpeg"},{"id":59862421,"identity":"1db046b6-c547-4bce-920a-fdac1529356d","added_by":"auto","created_at":"2024-07-08 14:59:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1074921,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4524554/v1/15f9528a-0f1b-48ba-b3b9-c15cc7b9947b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The roles of total cholesterol, the neutrophil-to- high-density-lipoprotein ratio and the lymphocyte-to-high-density-lipoprotein ratio in the diagnosis and progression of Parkinson's disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a neurodegenerative disease that is second after the most prevalent neurodegenerative disease, Alzheimer's disease. The global prevalence of PD increases with age and challenged in diagnosis of the disease. PD is also a kind of progressive neurological disease that currently lacks a cure with no treatments available that can effectively halt or prevent the disease's progression [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Aggregation of misfolded α-synuclein (α-SYN) is a characteristic pathological feature that leads to the formation of Lewy bodies. This process is followed by a significant loss of dopaminergic neurons in the substantia nigra pars compacta, a region of the midbrain. Although the exact mechanisms underlying the pathogenesis of PD are not yet completely understood, it is widely accepted that various pathological processes, including inflammation, immunity system dysregulation, oxidative stress and others, are likely to play crucial roles in both the development and progression of the disease [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt present, an extensive body of research indicates that inflammatory markers and the peripheral immune system are integral to the pathogenesis and progression of PD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is found that inflammation can induce or exacerbate the symptoms of PD. Microglia cells (MGs) play important roles in secreting proinflammatory mediators, and their activation can promote inflammation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This process could be overactivated by α-SYN aggregation, leading to a greater inflammatory response [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A growing number of studies have found that the integrity and function of the BBB are often destroyed during aging or in neurodegenerative diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Due to the impairment of the BBB in PD patients, peripheral inflammation spreads and leads to central inflammation, and the progression of PD can be delayed by inhibiting the proinflammatory T-cell response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In studies on animal models and PD patients, it was found that the infiltration of T cells in peripheral immunity and inflammatory factors increased in serum as well as the activation of glial cells and proinflammatory factors raised in the brain [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, several novel plasma markers have been identified, including the monocyte-to-high-density lipoprotein ratio (MHR) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the neutrophil-to-lymphocyte ratio (NLR) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the lymphocyte-to-monocyte ratio (LMR) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the neutrophil-to-monocyte ratio (NMR) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and the lymphocyte-to-high-density lipoprotein ratio (LHR) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These inflammatory biomarkers are pivotal in systemic inflammatory diseases, autoimmune disorders, metabolic syndrome, and oncology. They offer reliable stability for assessing systemic inflammation. Studies have shown that the NLR is closely associated with PD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; however, there is a scarcity of research on the neutrophil-to-high-density lipoprotein ratio (NHR) and the LHR in the relation to the onset and progression of PD. Consequently, this study aims to evaluate the correlation between these markers and PD, as well as their potential roles in the disease\u0026rsquo;s diagnosis and progression. The investigation of plasma inflammatory markers in PD not only provides a novel approach to understanding the pathophysiological mechanisms of PD but also holds significant implications for its diagnosis and treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003e The research protocol was approved by the Ethics Committee of Changsha Central Hospital, Hengyang Medical College, University of South China. According to the Declaration of Helsinki and the guidelines of the institute, all patients and their families signed informed consent forms and agreed to use their blood samples in the study.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: (1) the clinical and laboratory examination data were complete; (2) meeting the diagnostic criteria of the International Association of Parkinson's Disease and Movement Disorders Association. The exclusion criteria were as follows: (1) other neurodegenerative diseases, such as frontotemporal dementia and Alzheimer's disease; (2) infection; (3) severe liver and kidney injury; (4) history of stroke or head trauma; (5) complications, including diabetes, hypothyroidism, tumors and blood immune system diseases; and (6) long-term use of lipid-lowering drugs before onset.\u003c/p\u003e \u003cp\u003eThe study was performed on 192 patients with PD who were hospitalized in the Department of Neurology during the period from Jan 2020 to Aug 2023. Participants of healthy controls (HCs) were selected from the Health Examination Center at the Changsha Central Hospital, Hengyang Medical College, University of South China. The same exclusion criteria were applied to ensure comparability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMethods of blood analyses and clinical evaluation\u003c/h2\u003e \u003cp\u003eBasic information was collected, such as sex, age, BMI, smoking, drinking, hypertension, etc. Each patient diagnosed with PD underwent a comprehensive set of standardized assessments, which included the following measures: the Unified Parkinson\u0026rsquo;s Disease Rating Scale (UPDRS), which was comprised of three distinct subscales: UPDRS-I for assessing the patients\u0026rsquo; psychological well-being, UPDRS-II for examining their performance in daily activities, and UPDRS-III for evaluating motor capabilities. Additionally, the Hoehn and Yahr (H\u0026amp;Y) staging system was employed to gauge the clinical severity and monitor the progression of the disease in these patients. All subjects underwent blood sampling on the day they were admitted to the hospital. Blood tests included various components, including neutrophils, lymphocytes, monocytes, uric acid (UA), serum albumin, and blood lipids: triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL), etc. Subsequently, the following ratios were calculated: the NMR, the MHR, the LMR, the LHR, the NLR, the NHR and the HDL to LDL ratio (HLR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed utilizing SPSS 22.0 software (Chicago, USA). The normal distributions of measurement data were calculated by the Shapiro-Wilk test, and the results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). The analyses between the control group and PD group were used by t-test or Wilcoxon rank-sum test. And enumeration data were analyzed by chi-square test or Fisher\u0026rsquo;s exact test. In order to study the influencing factors of PD, univariate logistic regression was conducted firstly, followed by multivariate logistic regression. The results showed that statistically significant variables were as the independent influencing factors for PD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Spearman or Pearson correlation analyses were performed to investigate the relationships between plasma markers and the progression of PD. After conducting a multivariate logistic regression analysis to identify independent factors, a receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic value of these factors in predicting PD. Subsequently, a nomogram was constructed using the R programming language and the \"rms\" package. The accuracy of the nomogram was subsequently assessed using a bootstrapped-concordance index.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eThe demographic and clinical characteristics, along with laboratory features, of the control group and the PD group\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe demographic and clinical characteristics of both control individuals and patients are thoroughly detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study included 92 male (47.9%) and 100 female (52.1%) patients, aged between 34 and 78 years, with average age of 65.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9 years. In the control group, there were 91 males (50.6%) and 99 females (49.4%), with ages ranging from 33 to 79 years and an average age of 66.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 years. Patients with PD had an average body mass index (BMI) of 24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64 kg/m\u003csup\u003e2\u003c/sup\u003e, while HCs had an average BMI of 24.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 kg/m\u003csup\u003e2\u003c/sup\u003e. There were no significant differences in age (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.256) or BMI (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.087) between patients with PD and HCs. There were also no significant differences in sex (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.611), smoking (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.880) or drinking (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.799). The average H\u0026amp;Y staging score for patients with PD was 2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96. The mean UPDRS Part I score was 3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14, the mean UPDRS Part II score was 14.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09, and the mean UPDRS Part III score was 25.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12. And the mean duration of patients with PD was 53.66\u0026thinsp;\u0026plusmn;\u0026thinsp;49.40 months.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of healthy controls and 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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;190)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD patients (n\u0026thinsp;=\u0026thinsp;192)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et/Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\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)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(47.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (male, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking (male, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.66\u0026thinsp;\u0026plusmn;\u0026thinsp;49.40\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\u0026amp;Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\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\u003eUPDRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.21\u0026thinsp;\u0026plusmn;\u0026thinsp;11.35\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\u003eUPDRS I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\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\u003eUPDRS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\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\u003eUPDRS III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12\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\u003eSerum albumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294.48\u0026thinsp;\u0026plusmn;\u0026thinsp;82.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279.77\u0026thinsp;\u0026plusmn;\u0026thinsp;84.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \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\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHR (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHR (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/mmol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: PD Parkinson\u0026rsquo;s disease, BMI: body mass index; UA uric acid, TC total cholesterol, TG triglyceride, HDL high-density lipoprotein cholesterol, LDL low-density lipoprotein cholesterol, HLR HDL to LDL ratio, NMR neutrophil to monocyte ratio, LMR lymphocyte to monocyte ratio, NLR neutrophil to lymphocyte ratio, NHR neutrophil to HDL ratio, LHR lymphocyte to HDL ratio, MHR monocyte to HDL ratio, NHR neutrophil to HDL ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eComparative analysis of plasma markers in HCs and patients with PD\u003c/h2\u003e \u003cp\u003eTo learn the different factors between the PD group and HC group, the indicators were contrasted. Substantial differences were observed in plasma serum albumin, TC, TG, LDL, neutrophil counts, lymphocyte counts, monocyte counts, the HLR, the NHR, the LHR and the MHR between patients with PD and HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). And there were no significantly different in plasma uric acid (UA), HDL, the NMR, the LMR, and the NLR between the two groups (\u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequently, univariate logistic regression analysis revealed significant individual factors associated with PD, which were consistent with the substantial differences in these factors observed between the two groups. Moreover, a multivariate logistic regression analysis identified TC, the NHR and the LHR as independent influencing factors for PD. These factors, with TC (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.006, OR\u0026thinsp;=\u0026thinsp;0.602\u0026thinsp;\u0026lt;\u0026thinsp;1), the NHR (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.014, OR\u0026thinsp;=\u0026thinsp;0.731\u0026thinsp;\u0026lt;\u0026thinsp;1), and the LHR (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.029, OR\u0026thinsp;=\u0026thinsp;0.505\u0026thinsp;\u0026lt;\u0026thinsp;1), were significantly lower in PD patients compared to controls and were recognized as protective factors against PD, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \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 analyses of indicators for PD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.637 (0.509\u0026thinsp;~\u0026thinsp;0.797)\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\u003e-0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.624 (0.495\u0026thinsp;~\u0026thinsp;0.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.529 (0.397\u0026thinsp;~\u0026thinsp;0.704)\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\u003e-0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.718 (0.295\u0026thinsp;~\u0026thinsp;1.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0. 612 (0. 459\u0026thinsp;~\u0026thinsp;0.815)\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\u003e-0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.848 (0.607\u0026thinsp;~\u0026thinsp;1.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.074 (1.009\u0026thinsp;~\u0026thinsp;1.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.065 (0.995\u0026thinsp;~\u0026thinsp;1.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.910 (2.423\u0026thinsp;~\u0026thinsp;14.416)\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\u003e-0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.374 (0.063\u0026thinsp;~\u0026thinsp;2.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.776 (0.670\u0026thinsp;~\u0026thinsp;0.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.806 (0.690\u0026thinsp;~\u0026thinsp;0.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.507 (0.355\u0026thinsp;~\u0026thinsp;0.723)\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\u003e-0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.594 (0.405\u0026thinsp;~\u0026thinsp;0.871)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055 (0.011\u0026thinsp;~\u0026thinsp;0.272)\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\u003e-0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.397 (0.032\u0026thinsp;~\u0026thinsp;4.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe correlation between factors and stages of PD\u003c/h2\u003e \u003cp\u003ePlasma TC levels were negatively correlated with the UPDRS total score (r = -0.231, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), UPDRS-I score (r = -0.214, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and UPDRS-III score (r = -0.191, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the plasma NHR and LHR were also negatively correlated with the UPDRS total score (r = -0.357 and \u0026minus;\u0026thinsp;0.501, respectively, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), UPDRS-II score (r = -0.268 and \u0026minus;\u0026thinsp;0.365, respectively, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and UPDRS-III score (r = -0.332 and \u0026minus;\u0026thinsp;0.493, respectively, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, only the plasma LHR showed a negative correlation with the H\u0026amp;Y score (r = -0.198, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, the plasma NHR and LMR were negatively correlated with the disease duration in patients with PD (r = -0.249 and \u0026minus;\u0026thinsp;0.186, respectively, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, the plasma NLR was positively correlated with the duration of PD (r\u0026thinsp;=\u0026thinsp;0.195, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalyzing the correlation between variables and the progression of Parkinson's disease\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=\"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\u003eProcesses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUPDRS I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUPDRS II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUPDRS III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUPDRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH\u0026amp;Y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the nomogram and receiver operating characteristic (ROC) curve\u003c/h2\u003e \u003cp\u003eAfter univariate logistic regression analysis, multivariate analysis revealed that TC, NHR and LHR were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The model\u0026rsquo;s effectiveness in identifying patients at high risk for developing PD was supported by an area under the curve (AUC) of 0.682 (95% CI: 0.629 to 0.735) in the ROC curve, with a sensitivity of 72.9% and a specificity of 57.4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, a nomogram chart was constructed using R language and the \u0026ldquo;rms\u0026rdquo; package. The top section of the chart assigns points to each factor, including TC, NHR, and LHR. Directly beneath the total points, the chart displays the corresponding estimated probability percentage of developing PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The nomogram demonstrated a bootstrap-corrected concordance index of 0.682, with a strong correlation between the actual and predicted probabilities, indicating its excellent discriminative ability, and it was well calibrated with observed outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we collected data from individuals with PD and compared it with a group of HCs matched by age and sex. Notably, significant differences in several biomarkers were observed between the PD patients and the control group. We then utilized logistic regression, correlation analysis and nomogram analysis to investigate factors associated with PD, identifying inflammatory markers and blood lipid profiles as key contributors to the disease. Our thorough examination highlighted the significance of TC, NHR, and LHR as pivotal factors for diagnosing and tracking PD progression.\u003c/p\u003e \u003cp\u003eBlood lipids, encompassing TC, TG, LDL and HDL, are integral to the pathogenesis of neurodegenerative diseases, as evidenced by substantial research [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Disruptions in lipid metabolism can lead to an overproduction of free radicals, which in turn compromise the body's antioxidant defenses. This results in heightened oxidative stress, causing alterations in blood rheology, thickening of the microvascular walls, and the development of vitreous lesions. These changes can culminate in vascular occlusion, leading to cerebral ischemia, hypoxia, and neurodegeneration [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, dyslipidemia is recognized as a potential harbinger of motor symptoms in patients with PD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Existing literature on the link between blood lipids and PD yields mixed results. While Lu et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] reported no significant differences in TC, TG, and LDL levels between PD patients and healthy controls, Saedi et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] observed significantly reduced serum concentrations of these lipids in PD patients, including TG, LDL and TC. There is a hypothesis that inadequate or dysfunctional HDL within the brain contributes to neurodegenerative pathology [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the current study, multivariate regression analysis identified TC was a significant predictor in the diagnosis of PD. We found that patients diagnosed with PD had lower TC levels than those in the control group, consist with the results from Saedi\u0026rsquo;s research [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, a significant correlation was observed between TC levels and the severity of PD as assessed by the UPDRS scores. This correlation indicates that TC may be a potential biomarker for the progression of PD. Therefore, blood lipids have important roles in the progression and pathogenesis of PD.\u003c/p\u003e \u003cp\u003eRecent studies have firmly linked inflammation with the pathogenesis of PD [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The LHR is a novel indicator of inflammation, which extends to metabolic syndrome (METS) and chronic obstructive pulmonary disease, where it is an independent risk factor [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. METS has been linked to increased leukocyte and lymphocyte counts, suggesting LHR\u0026rsquo;s importance in assessing its presence and severity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Studies have also shown that both the LHR and the NHR are predictive of METS in women, irrespective of other risk factors [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the context of PD, lymphocyte counts are reduced, particularly CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD19\u003csup\u003e+\u003c/sup\u003e B cells, and Treg cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Analysis of 123 newly diagnosed PD patients showed an inverse relationship between lymphocyte and neutrophil percentages and the motor score on the UPDRS [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. While a cohort study indicated that higher lymphocyte counts are tied to a lower PD risk, it was acknowledged to have confounding factors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the indicator LHR in PD research has not yet been investigated. In this study, through multivariate logistic regression analysis, we found that PD patients had lower LHR than controls, positing it as a protective factor. Correlation analyses, including Pearson and Spearman, demonstrated significant negative correlations between LHR and UPDRS scores, the H\u0026amp;Y staging system, and disease duration in PD patients. Collectively, these findings suggest that the LHR could serve as a potential biomarker, aiding in the diagnosis of PD and potentially predicting the progression of the disease.\u003c/p\u003e \u003cp\u003eA previous study found that NHR could predict METS in women [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. NHR is also a biomarker for nascent METS [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In a comparison study about acute myocardial infarction, NHR had a superior prognostic value in elderly patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, there are few studies about NHR in PD. In this study, we discovered that the NHR was significantly lower in patients with PD compared to HCs. Multivariate logistic regression analysis further supported NHR as a protective factor. Additionally, Spearman correlation analyses revealed significant negative correlations between NHR and certain components of the UPDRS scores. These findings suggest that the NHR could serve as a potential biomarker for predicting the progression of PD\u003c/p\u003e \u003cp\u003eMany studies have shown that the MHR is closely related to the occurrence, progress and prognosis of cardiovascular, immune system diseases and rheumatic diseases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, when we compared the two groups, we found that the MHR acted as a protective factor against PD, with significantly lower levels observed in patients diagnosed with PD. However, MHR did not emerge as an independent factor that could influence the diagnosis of the disease. Additionally, no significant differences were found in UA levels, the LMR and NLR between PD patients and HCs. However, both the NLR and LMR demonstrated significant correlations with disease duration in PD patients, indicating a potential link that warrants further investigation to understand the underlying mechanisms.\u003c/p\u003e \u003cp\u003eIn summary, our study delineated the roles and clinical relevance of inflammatory markers and lipid profiles in the diagnosis and progression of PD. Specifically, the LHR and NHR emerged as novel biomarkers with potential implications for both diagnosing PD and tracking its progression.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eNevertheless, this study has certain limitations. Firstly, it was conducted as a single-center pilot study, which may affect the generalizability of the findings. Secondly, the research did not delve into the cellular and molecular mechanisms that could explain how inflammation impacts the development and course of PD, an area that requires further exploration.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our research has identified the plasma biomarkers TC, NHR, and LHR as key indicators in the diagnostic process and in monitoring the progression of PD. The ease of deriving these markers from standard blood tests renders them readily available for clinical application, which may improve the precision of diagnosis. However, further investigation is necessary to establish a clear link between these biomarkers and the underlying mechanisms of PD. This understanding could pave the way for more targeted and effective treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYPT\u0026nbsp;and SFC designed the study, and YPT and BL performed the statistical analyses, interpreted the results, and wrote the manuscript. WX, JH, FYL, LL participated in collecting and analysing the data, resolving difficulties in the analytic strategies and discussing the results. Finally, YPT and SFC were the final reviewers and acted as the corresponding authors. All of the authors have read as well as approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Hunan Provincial Health Commission foundation (20201928; to YPT), the Changsha Bureau of Science and Technology\u0026nbsp;(kzd2001077; to YPT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets employed and/or analyzed within the current research can be accessed from the corresponding authors upon reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to show our gratitude to all those who were involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny field of the research involved within this manuscript which covered patients of human was performed after obtaining ethics approval from all related bodies\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants as well as authors have given their consent for the publication of this paper in Lipids in Health and Disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan Munster M, Stumpel J, Thieken F, et al. 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Clin Transl Oncol. 2019;21(tissue):855\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaricam G. Relationship between migraine headache and hematological parameters. Acta Neurol Belg. 2021;121(7):899\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S, Guo X, Li G, Yang H, Zheng LSun Y. Lymphocyte to High-Density Lipoprotein Ratio but Not Platelet to Lymphocyte Ratio Effectively Predicts Metabolic Syndrome Among Subjects From Rural China. Front Cardiovasc Med. 2021;8:583320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Xiong C, Shao X, et al. Lymphocyte To High-Density Lipoprotein Ratio As A New Indicator Of Inflammation And Metabolic Syndrome. Diabetes Metab Syndr Obes. 2019;12:2117\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Fan Q, Wu S, Wan YLei Y. 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Antioxid (Basel). 2022;11(3):524.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Jin XZhao P. Serum lipids and the pathogenesis of Parkinson's disease: A systematic review and meta-analysis. Int J Clin Pract. 2021;75(4):e13865.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang F, Zhan Y, Hammar N, et al. Lipids, Apolipoproteins, and the Risk of Parkinson Disease. Circ Res. 2019;125(6):643\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaedi S, Hemmati-Dinarvand M, Barmaki H, et al. Serum lipid profile of Parkinson's disease patients: A study from the Northwest of Iran. Casp J Intern Med. 2021;12(2):155\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahrami A, Barreto GE, Lombardi G, Pirro MSahebkar A. Emerging roles for high-density lipoproteins in neurodegenerative disorders. 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Ann Neurol. 2019;86(6):913\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJialal I, Jialal G, Adams-Huet BRamakrishnan N. (2020) Neutrophil and monocyte ratios to high-density lipoprotein-cholesterol and adiponectin as biomarkers of nascent metabolic syndrome. Horm Mol Biol Clin Investig 41(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang JB, Chen YS, Ji HY, et al. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids Health Dis. 2020;19(1):59.\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, inflammatory markers, blood lipids, diagnosis, progression","lastPublishedDoi":"10.21203/rs.3.rs-4524554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4524554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eParkinson\u0026rsquo;s disease (PD), is the second most prevalent neurodegenerative disease after Alzheimer\u0026rsquo;s disease. Inflammation, immunity dysregulation and other pathological processes play pivotal roles in the pathogenesis and progression of PD. Nevertheless, the role of peripheral inflammatory markers in diagnosing and monitoring the progression of PD remains to be fully elucidated.\u003c/p\u003e\u003ch2\u003ePatients and Methods\u003c/h2\u003e \u003cp\u003e192 patients with PD were selected, and sex- and age-matched healthy individuals (n\u0026thinsp;=\u0026thinsp;190) were included in the control group. Then, the persons\u0026rsquo; basic information was collected, such as gender, age, smoking, blood lipids, and so on. Then, the neutrophil-to-high-density-lipoprotein ratio (NHR) and the lymphocyte -to-high-density-lipoprotein ratio (LHR), etc. were calculated. Then, the indicators were contrasted in the two groups, and, univariate and multivariate logistic regression were conducted. Pearson and Spearman correlation analyses were utilized to determine the correlation between total cholesterol (TC), the NHR, the LHR, the neutrophil-to-lymphocyte ratio (NLR), the lymphocyte-to-monocyte ratio (LMR), and the progression of PD. The nomogram was drawn using R language.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTC, triglyceride (TG), neutrophils, monocytes, lymphocytes, serum albumin, the high-to-low-density-lipoprotein ratio (HLR), the monocyte-to-high-density-lipoprotein ratio (MHR), the NHR and the LHR in the PD group were significantly different from those in the control group (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). Univariate and multivariate logistic regression analyses showed that TC, the LHR, and the NHR were independent influencing factors for PD. Pearson and Spearman correlation analyses indicated a negative correlation between TC, the NHR, the LHR and the UPDRS scores in patients with PD. And it showed a negative correlation between the LHR and the Hoehn and Yahr (H\u0026amp;Y) staging system. Moreover, it showed significant correlations between the LHR, the NLR, the LMR, and the duration of patients with PD. Furthermore, an accuracy model of the nomogram was structured for the indicators of PD, which showed adequately sensitivity and specificity using receiver operating characteristic (ROC) curve to evaluate the diagnostic performance of TC, the LHR, and the NHR for PD.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings indicate that lower levels of TC, the NHR, and the LHR may be relevant for diagnosing and assessing the progression of PD, and they appear to be candidate biomarkers for PD.\u003c/p\u003e","manuscriptTitle":"The roles of total cholesterol, the neutrophil-to- high-density-lipoprotein ratio and the lymphocyte-to-high-density-lipoprotein ratio in the diagnosis and progression of Parkinson's disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 20:17:19","doi":"10.21203/rs.3.rs-4524554/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":"097adb88-5a84-476b-b0e8-64d867c6f3ba","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-08T14:59:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-25 20:17:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4524554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4524554","identity":"rs-4524554","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}