Neutrophil percentage-to-albumin ratio increases the risk of Parkinson’s disease and all-cause mortality in Parkinson’s disease patients: A population-based Study

Neutrophil percentage-to-albumin ratio increases the risk of Parkinson’s disease and all-cause mortality in Parkinson’s disease patients: A population-based Study | 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 Neutrophil percentage-to-albumin ratio increases the risk of Parkinson’s disease and all-cause mortality in Parkinson’s disease patients: A population-based Study Fujun Liu, UMAR FAROOQ, Sijun Diao, Zhongyu Li, Qibo Ran, Jing Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311621/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: This study aims to investigate the association of neutrophil percentage-to-albumin ratio (NPAR) with Parkinson’s disease (PD) prevalence and all-cause mortality risk in PD patients. Methods: Using NHANES 2001-2018 data (n=25,170; 309 PD patients), weighted multivariate logistic regression and Cox regression analyses were applied to evaluate the associations between NPAR and the prevalence of PD, as well as between NPAR and all-cause mortality risk. Restricted cubic spline (RCS) analysis elucidated the precise relationships. Consistency of results was checked through subgroup analysis. Results: The mean NPAR was higher in PD (14.78±0.21) vs. that in non-PD (14.01±0.03; p < 0.001). After adjusting all variables, each unit increase in NPAR corresponded to 8% higher PD odds (OR=1.08, 95%CI:1.02-1.15, p=0.01). The prevalence of PD in the highest quintile (Q3) was 1.54 times higher than that in the lowest quintile (Q1) (OR=1.54; 95% CI, 1.02-2.34, p=0.042). The RCS analysis confirmed a linear dose-response relationship (non-linear p=0.8598), with PD risk increasing progressively at NPAR levels above approximately 13.95 (P-overall=0.0217). For all-cause mortality in PD patients, NPAR was also significantly associated. In the fully adjusted model, each unit increase in NPAR was linked to a 9% higher hazard of death (HR=1.09, 95%CI: 1.02–1.17, p=0.015), and patients with highest Q3 had an 85% higher mortality risk compared to the Q1 (HR=1.85, 95% CI: 1.03–3.33, p=0.040). Furthermore, Kaplan-Meier analysis demonstrated that PD patients in the Q1 exhibited the highest survival rates (Log-rank test, p=0.042). RCS analysis revealed a linear association (non-linear p=0.1835) for mortality, with increased risk evident above NPAR levels of approximately 14.6 (p-overall=0.0145). Subgroup analyses and interaction tests demonstrated the robustness of this association (p-interaction>0.05). Conclusions: NPAR exhibits a linear, positive association with PD risk in the general population and with all-cause mortality in PD patients. Higher NPAR levels, particularly above 13.95 for PD risk and 14.6 for all-cause mortality, are associated with increased risk. These findings highlight the potential of NPAR as a biomarker for risk stratification and prognosis in PD. Parkinson's disease National Health and Nutrition Examination Survey Neutrophil percentage-to-albumin ratio NPAR mortality risk Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Parkinson's disease (PD) ,the second most prevalent neurodegenerative disorder, is characterized by motor symptoms such as bradykinesia, resting tremor, and rigidity, as well as non-motor manifestations including cognitive decline and sleep disturbances [1,2]. The accelerating global aging of population has led to a notable increase in the incidence and prevalence of PD, with an estimated prevalence of approximately 4% in individuals aged 60 and above [3,4,5]. This escalating trend imposes a great challenge on both individuals and healthcare systems, highlighting the urgent need for effective strategies to identify high-risk populations early and enable timely interventions to improve patient outcomes and reduce socioeconomic burden. The pathological feature of PD is the progressive degeneration of dopamine-producing neurons within the substantia nigra pars of the midbrain, alongside the intracellular buildup of α-synuclein and the subsequent formation of Lewy bodies in remaining dopaminergic neurons[6]. Recent research has highlighted the critical role of neuroinflammation in PD pathophysiology [6]. However, the precise role of inflammation in neurodegeneration, specifically whether it serves as a primary cause or a consequence of neuronal damage, remains a subject of active debate and ongoing research[7,8]. Nonetheless, inflammatory processes are believed to disrupt the blood-brain barrier, allowing immune cells to enter the central nervous system, leading to neuronal damage and worsening disease progression[9]. Furthermore, recent studies suggest that inflammation also plays a role in the development of atypical parkinsonism[10]. For instance, studies have indicated specific interleukin patterns in progressive supranuclear palsy, highlighting the diverse inflammatory landscapes across neurodegenerative conditions[10]. Consequently, peripheral blood inflammatory biomarkers have attracted considerable attention for their potential utility as diagnostic and prognostic indicators across a broad spectrum of medical conditions[11,12]. Previous studies on PD have investigated the diagnostic and prognostic significance of inflammation-related or nutritional markers, including neutrophil, lymphocyte, monocyte counts [13], albumin-to-fibrinogen ratio[14], and systemic inflammation index [11]. However, these existing markers often offer a limited, singular perspective on the complex interplay between systemic inflammation and nutritional status, failing to fully capture the dynamic interplay between immune activation and host metabolic/nutritional reserves. The neutrophil percentage-to-albumin ratio (NPAR) is a prominent emerging composite biomarker that holistically reflects both systemic inflammatory burden and nutritional status[15]. NPAR is defined as the ratio of neutrophil percentage to serum albumin concentration[16]. Given the pivotal role of neutrophils in mediating inflammatory responses and the close association of serum albumin levels with overall nutritional well-being and immune regulation, an elevated NPAR is suggested to indicate heightened systemic inflammation or impaired nutritional status. Unlike single inflammatory markers or simple ratios (e.g., lymphocyte, monocyte, platelet-to-lymphocyte ratio) that capture only one facet of systemic response, NPAR's unique advantage lies in its ability to simultaneously integrate two critical, yet often opposing, dimensions: the pro-inflammatory drive (neutrophil percentage) and the anti-inflammatory/nutritional protective capacity (albumin)[17]. This dual-dimensional information offers a more comprehensive and nuanced assessment of the overall systemic biological perturbation. Indeed, previous studies have shown that NPAR's significant prognostic utility in conditions such as diabetes mellitus(DM), cardiovascular diseases, and various malignancies[17, 18,19]. To our knowledge, the precise relationship between NPAR and the risk of PD prevalence and its association with all-cause mortality in PD patients are both unclear. Therefore, this study aims to systematically examine the relationship between NPAR and the risk of PD prevalence and all-cause mortality among PD patient, alongside the impact of different population characteristics on the all-cause mortality among PD patients in the 2001–2018 National Health and Nutrition Examination Survey (NHANES) database. By comprehensively analyzing this biomarker, which is both readily accessible and cost-effective, this research seeks to provide a novel tool for early risk stratification and precision prevention and intervention of PD. Methods Study Design and Data Sources The National Center for Health Statistics administers NHANES, a biennial cross-sectional survey, to assess U.S. population health by collecting data from randomly selected non-institutionalized individuals. The NHANES project was approved by the National Center for Health Statistics Institutional Review Board, and all participants provided written informed consent. This study used publicly accessible, de-identified NHANES data, eliminating the need for further local ethics committee approval according to our institutional guidelines. The study adhered to the STROBE Statement guidelines for observational research reporting. Data for this study were obtained from eight consecutive two-year cycles of NHANES, spanning from 2001 to 2018. The study initially included 91,351 participants. Participants under 40 years old (n=59,182) were excluded to maintain clinical relevance for PD research, leaving 32,169 individuals. Subsequently, participants with missing follow-up data (n=1570), unavailable NPAR data (n=1992), and unavailable PD data (n=33) were excluded, totaling 3,595 exclusions and leaving 28,574 individuals. Furthermore, those with missing data for other key covariates (n= 3,404) were excluded. This rigorous selection process yielded a final analytical sample of 25,170 eligible participants for this study. Fig . 1 illustrates the participant selection process in detail. Definitions of exposure The primary exposure variable in this study was the NPAR. NPAR is a composite biomarker derived from the percentage of neutrophils in the blood and the serum albumin concentration. NPAR is defined as the ratio of neutrophil percentage to serum albumin concentration (expressed in g/dL)[16]. Neutrophil percentage was measured using the Beckman Coulter Automated Hematology Analyzer DxH 900, adhering to the standardized NHANES laboratory protocol. Serum albumin concentration was determined using the biochromatic digital endpoint method on the LX20 analyzer. Study subjects were stratified into tertiles according to NPAR thresholds: Q1 ( 15), utilizing Q1 as the comparator cohort. Covariates This study selected and incorporated covariates for statistical adjustment based on existing literature and clinical relevance to effectively control for potential confounding. These covariates encompassed multiple dimensions: demographic factors included such as age, sex (male/female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Race), education level (three categories), marital status (two categories), and poverty income ratio(PIR) (two categories); behavioral factors primarily comprised smoking status (yes/no); clinical factors involved body mass index (three categories), history of stroke, DM, coronary heart disease, hyperlipidemia, and hypertension (all defined as yes/no); and laboratory factors included red blood cell count and serum creatinine (both continuous). Notably, to avoid collinearity with NPAR, which is the primary exposure variable, its constituent components—neutrophil percentage and serum albumin—were not included as separate covariates in the multivariable models. Covariate data were sourced from questionnaires, physical exams, and lab measurements in the NHANES database. Comprehensive details are available on the NHANES website (www.cdc.gov/nchs/nhanes). Determination of PD PD was identified through participants' self-reported use of anti-Parkinson medications. Participants were identified as having PD if they reported current use of any medication under the "Anti-Parkinson Agents" category within the NHANES Prescription Medications data. These drug categories include, but are not limited to, levodopa, carbidopa, entacapone, pramipexole, and amantadine[20]. Individuals not reporting the use of anti-Parkinson medications were classified as non-PD. This operational definition of PD, based on self-reported anti-Parkinson medication use, aligns with established methods from previous NHANES-based studies. This approach is widely accepted as it effectively addresses the limitation of lacking clinical PD diagnoses in NHANES data and is considered a valid strategy for PD-related research using this database [11,21]. Follow-up and outcome assessment The primary outcome measures were the prevalence of PD and all-cause mortality in PD patients. Participant vital status was confirmed using the National Death Index. Participant status follow-up data in the NHANES database were systematically updated monthly. The collected follow-up data included participants' survival status at each assessment, survival duration, causes of death, and other pertinent demographic and health information. Causes of death were classified into established categories, including traffic accidents, heart disease, pneumonia and influenza, kidney disease, chronic respiratory diseases, cerebrovascular accidents, DM, malignant neoplasms, and other causes. All-cause mortality refers to any death from any cause during the observation period. Statistical Analysis The study's statistical analyses incorporated NHANES's complex sampling design and applied weighting to analyses. Continuous variables are presented as weighted means with standard deviations, and categorical variables as weighted percentages. Weighted independent samples t-tests and weighted chi-square tests were employed to compare baseline characteristics between groups for continuous and categorical variables, respectively. We employed weighted multivariate logistic regression models to assess the association between NPAR and PD prevalence, presenting adjusted odds ratios (OR) with 95% confidence intervals (CI) to quantify the strength and significance of the relationship. In the regression analyses, NPAR was analyzed both continuously and categorically, using its quartiles within the study population. Several models were constructed to progressively adjust for potential confounding factors. After establishing the relationship between NPAR and the prevalence of PD, we selected the PD cohort and further examined the association between NPAR and all-cause mortality using multivariate Cox regression. The hazard ratio (HR) served as the primary measure to quantify this relationship with mortality, where an HR value below 1 indicated a protective influence, and an HR exceeding 1 suggested an increased risk. For the multivariate Cox regression analysis, three distinct models were employed to meticulously address and adjust for potential confounding effects. Kaplan–Meier survival curves were generated to compare survival rates among different PD groups (Q1, Q2 and Q3) using the Log rank test. Finally, the quantitative assessment of the relationship between NPAR and clinical outcomes was performed via restricted cubic spline (RCS) analysis. RCS curves were generated to pinpoint precise NPAR levels that exhibited significant associations with adverse outcomes. Subgroup analyses were performed to evaluate if the association between NPAR and mortality among PD patients remains consistent across various subgroups. The models incorporated interaction terms to formally assess effect modification. Statistical analyses were conducted using R software (version 4.4.2). A two-sided p-value below 0.05 was set as the criterion for statistical significance. Results Demographic and clinical features of participants The final study included 25,170 participants, 309 of whom were diagnosed with PD. Participants were categorized into PD and non-PD groups according to the established screening criteria for PD. Weighted demographic, lifestyle, and biomarker characteristics are presented in Table 1. PD patients were significantly older than those non-PD patients. A borderline significant difference was observed in sex distribution, with a higher proportion of females in the PD group (59.86%) compared to the non-PD group (52.35%). Significant differences were also noted in ethnicity and PIR, with White individuals and those in the lowest income bracket being more prevalent in the PD cohort. Regarding lifestyle and health conditions, PD patients exhibited a significantly higher prevalence of stroke, DM, and hypertension. BMI also differed significantly, with a higher proportion of PD patients categorized as obese. In terms of laboratory tests, neutrophil percentage, and creatinine were significantly higher in the PD group, while albumin and red blood cell count were significantly lower. No significant differences were observed for marital status, education level, smoking status, hyperlipidemia, coronary heart disease, ALT, AST, globulin, phosphorus, uric acid, sodium, and total calcium. Interestingly, NPAR levels were significantly higher in the PD group than in the non-PD group, indicating that NPAR might serve as a risk factor. Table 1. Weighted characteristics of study participants from the NHANES, 2001-2018. Variable Total Non-Parkinson Parkinson p value No. 25170 24861 309 Age 57.80±0.15 57.76±0.14 61.32±1.02 < 0.001 Sex 0.05 Female 12745(52.43) 12588(52.35) 157(59.86) Male 12425(47.57) 12273(47.65) 152(40.14) Marital status, n(%) 0.09 Married 15771(68.31) 15600(68.38) 171(62.39) unmarried 9399(31.69) 9261(31.62) 138(37.61) Education level, n(%) 0.65 Less than high school 3402(6.36) 3364(6.36) 38(6.40) High school 9331(34.34) 9210(34.31) 121(36.99) More than high school 12437(59.30) 12287(59.33) 150(56.60) Ethnicity, n(%) 0.03 White 12212(74.69) 12013(74.61) 199(81.60) Mexican American 3709(5.59) 3675(5.61) 34(4.59) Black 5074(9.49) 5031(9.51) 43(7.38) Other 4175(10.22) 4142(10.27) 33(6.43) Poverty income ratio 0.001 0-1.5 8345(20.68) 8214(20.57) 131(30.63) >=1.5 16825(79.32) 16647(79.43) 178(69.37) Smoke 0.59 no 12632(50.83) 12485(50.81) 147(52.65) yes 12538(49.17) 12376(49.19) 162(47.35) BMI 0.02 =30 9747(38.33) 9596(38.21) 151(47.80) Stroke < 0.0001 no 23765(95.77) 23497(95.86) 268(87.35) yes 1405(4.23) 1364(4.14) 41(12.65) Hyperlipidemia 0.6 no 5029(19.80) 4972(19.82) 57(18.04) yes 20141(80.20) 19889(80.18) 252(81.96) DM 0.02 no 20463(85.89) 20236(85.96) 227(80.04) yes 4707(14.11) 4625(14.04) 82(19.96) Coronary heart disease 0.07 no 23544(94.45) 23269(94.49) 275(91.53) yes 1626(5.55) 1592(5.51) 34(8.47) Hypertension 0.02 no 1102(4.31) 1095(4.34) 7(1.59) yes 24068(95.69) 23766(95.66) 302(98.41) NPAR 14.01±0.03 14.01±0.03 14.78±0.21 < 0.001 Neutrophil percentage 58.78±0.10 58.75±0.10 60.66±0.68 0.01 Albumin 4.22±0.00 4.22±0.00 4.13±0.03 0.003 Red blood cell 4.66±0.01 4.66±0.01 4.56±0.04 0.01 Alt 25.09±0.14 25.10±0.14 24.30±1.60 0.62 Ast 25.63±0.13 25.62±0.13 26.61±1.55 0.52 Globulin_g.dl 2.85±0.01 2.85±0.01 2.82±0.04 0.41 Creatinine_mg.dl 0.92±0.00 0.92±0.00 0.97±0.02 0.03 Phosphorus_mmol.L 1.20±0.00 1.20±0.00 1.21±0.02 0.8 Uric_acid_umol.L 326.62±0.80 326.55±0.80 332.28±6.26 0.36 Sodium_mmol.L 139.30±0.06 139.30±0.06 139.20±0.18 0.52 Calcium_total_mg.dl 9.42±0.01 9.42±0.01 9.38±0.03 0.16 AST, aspartate aminotransferase; ALT, alanine aminotransferase; BMI, body mass index; DM; No., number; NPAR: neutrophil percentage-to-albumin ratio; OR, odds ratio The association between NPAR and prevalence of PD We employed weighted multivariate logistic regression analysis to determine the relationship between NPAR and PD prevalence, calculating the OR and 95% CI ( Table 2 ). Both NPAR as a continuous variable and as quartile were assessed across four models: the Crude Model (unadjusted), Model 1 (adjusted for demographics), and Model 2 (adjusted for all covariates). As a continuous variable, NPAR was consistently and significantly associated with an increased prevalence of PD across all models. In the Crude Model, each unit increase in NPAR was associated with 12% increased odds of PD (OR = 1.12, 95% CI: 1.06-1.19, p < 0.001). This association remained statistically significant, albeit with slightly attenuated odds ratios, after progressive adjustment for covariates: Model 1 (OR = 1.10, 95% CI: 1.03-1.17, p = 0.003), and Model 2 (OR = 1.08, 95% CI: 1.02-1.15, p = 0.01). When NPAR was categorized into quartiles, with the Q1 as the reference, the Q3 consistently demonstrated a significantly increased odds of PD across all models. Specifically, the odds ratios for Q3 were: 1.94 (95% CI: 1.30-2.91, p = 0.001) in the Crude Model, 1.72 (95% CI: 1.13-2.61, p = 0.012) in Model 1, and 1.54 (95% CI: 1.02-2.34, p = 0.042) in Model 2. Importantly, a significant p for trend was observed across all models (p < 0.05), indicating a dose-response relationship where higher NPAR quartiles were associated with an increased risk of PD. Table 2. Multivariate weighted logistic regression analysis of the association between NPAR and the prevalence of PD NPAR~Parkinson Crude Model* Model 1* Model 2* Character OR 95%CI p OR 95%CI p OR 95%CI p Continuous 1.12(1.06,1.19) <0.001 1.10(1.03,1.17) 0.003 1.08(1.02,1.15) 0.01 NPAR quartile Q1 Ref. Ref. Ref. Q2 1.42(0.92,2.18) 0.114 1.35(0.87,2.09) 0.175 1.32(0.86,2.03) 0.208 Q3 1.94(1.30,2.91) 0.001 1.72(1.13,2.61) 0.012 1.54(1.02,2.34) 0.042 p for trend 0.001 0.011 0.043 NPAR: neutrophil percentage-to-albumin ratio. The crude model* is devoid of covariates. Model 1* contains only age, education, sex, ethnicity, marital status, and poverty income ratio. Model 2* incorporates all covariates. Positive association of NPAR with all-cause mortality in PD To determine if NPAR continues to serve as a risk factor for individuals already diagnosed with PD, we conducted survival analyses employing cox regression models and Kaplan-Meier curves. Cox regression analysis ( Table 3 ) examined the association between NPAR, both as a continuous variable and categorized into tertile groups (Q1, Q2, Q3, with Q1 serving as the reference), and all-cause mortality. We constructed three sequential models: Model 4 (unadjusted), Model 5 (partially adjusted), and Model 6 (fully adjusted for all covariates considered in this study). As a continuous variable, NPAR was consistently and significantly associated with an increased risk of all-cause mortality in PD patients across all models. In the Model 4, each unit increase in NPAR was associated with a 14% increased hazard of all-cause mortality (HR = 1.14, 95% CI: 1.07–1.22, p < 0.001). This association remained statistically significant, albeit with slightly attenuated hazard ratios, after progressive adjustment for covariates: Model 5(HR = 1.12, 95% CI: 1.04–1.20, p = 0.002), and Model 6 (HR = 1.09, 95% CI: 1.02–1.17, p = 0.015) When NPAR was categorized into tertiles, with Q1 as the reference, the highest Q3 consistently demonstrated a significantly increased risk of all-cause mortality in PD across all models. Specifically, the hazard ratios for Q3 were: 1.87 (95% CI: 1.11–3.15, p=0.019) in Model 4, 1.73 (95% CI: 1.01–2.98, p=0.046) in Model 5, and 1.85 (95% CI: 1.03–3.33, p=0.040) in Model 6. In contrast, the middle tertile (Q2) did not show a statistically significant association with all-cause mortality in any model (HR=1.44, 95% CI: 0.82–2.53, p=0.203 in Model 4; HR=1.66, 95% CI: 0.92–3.01, p=0.093 in Model 5; HR=1.77, 95% CI: 0.94–3.39, p=0.076 in Model 6), suggesting the effect is primarily driven by the highest exposure group. Kaplan–Meier analysis showed, in the PD cohort, the highest survival rate in Q1 group, with significant differences noted (p= 0.042) (Fig. 2) . In contrast, those in the highest NPAR tertile (Q3, represented by the blue line) consistently showed the lowest survival probability. The survival curve for the middle NPAR tertile (Q2) fell between Q1 and Q3, generally tracking closer to Q3, particularly in the later stages of the observation period. These findings collectively indicate that higher NPAR levels are associated with reduced survival probabilities in the studied cohort. Table 3. Multivariate cox regression analysis of NPAR and All-cause mortality in PD patients Model NPAR HR 95% CI p-value Model 4* Continuous 1.14 1.07, 1.22 <0.001 Q1 Q2 1.44 0.82, 2.53 0.203 Q3 1.87 1.11, 3.15 0.019 Model 5* Continuous 1.12 1.04, 1.20 0.002 Q1 Q2 1.66 0.92, 3.01 0.093 Q3 1.73 1.01, 2.98 0.046 Model 6* Continuous 1.09 1.02, 1.17 0.015 Q1 Q2 1.77 0.94, 3.39 0.076 Q3 1.85 1.03, 3.33 0.040 Model4*:no confounding factors were adjusted. Model 5* contains only age, education, sex, ethnicity, marital status, and poverty income ratio. Model 6* incorporates all covariates in this study. HR, Hazard Ratio; CI, Confidence Interval. Subgroup analysis and interaction analysis After establishing NPAR as a risk factor for all-cause mortality, we further conducted subgroup and interaction analyses to evaluate the robustness of this association and potential effect modification in the PD cohort ( Fig.3 ). Subgroups were defined by age, gender, ethnicity, marital status, education level, PIR, BMI, history of stroke, hyperlipidemia, DM, coronary heart disease, and smoking status. The subgroup analysis demonstrated that higher NPAR levels were significantly associated with increased all-cause mortality in participants aged ≥65 years (p=0.040), those of Mexican American ethnicity (p=0.042), those with a PIR <1.5 (p=0.041), those without a history of stroke (p=0.037), those with hyperlipidemia (p=0.031), those without DM (p=0.041), and non-smokers (p=0.026). No statistically significant associations (p< 0.05) were observed in the corresponding complementary subgroups, nor in the subgroups of White ethnicity (p=0.053), married individuals (p=0.054), or individuals without coronary heart disease (p=0.054). Similarly, no significant associations were found when stratifying by gender, education level, or BMI. Interaction analyses showed that NPAR was an independent factor influencing all-cause mortality, with no statistically significant interactions for all variables (all p- interaction >0.05). Quantitative evaluation of NPAR Levels and Adverse Outcomes via RCS regression We further explored the quantitative relationships between NPAR and adverse outcomes using RCS regression analysis, as presented in Fig 4 . Panel A illustrates the dose-response relationship between NPAR and the risk of PD in the general population. The overall p-value for the association was statistically significant (p-overall = 0.0217). However, the test for non-linearity was not significant (NL-p value = 0.8598), indicating a linear relationship. The adjusted OR for PD risk was observed to increase linearly with higher NPAR values. Specifically, the odds of developing PD progressively increased when NPAR values surpassed approximately 13.95. Panel B depicts the dose-response relationship between NPAR and all-cause mortality in PD patients. A significant overall association was also identified (p-overall = 0.0145). Similar to Panel A, the non-linearity test was not significant (NL-p value = 0.1835), confirming a linear relationship. The adjusted HR for all-cause mortality in PD linearly increased with rising NPAR levels. An increased risk of all-cause mortality became evident when NPAR values exceeded approximately 14.6. These quantitative analyses demonstrate that higher NPAR levels are linearly and significantly associated with an elevated risk of both PD incidence and all-cause mortality in PD patients. The findings suggest that maintaining NPAR levels below approximately 13.95 for PD risk and 14.6 for all-cause mortality may be beneficial. Discussion This population-based study firstly systematically examined the association between NPAR and both PD prevalence and all-cause mortality in PD patients using NHANES 2001-2018 data. Our findings demonstrate that higher NPAR levels are significantly associated with an increased prevalence of PD in the general population. Specifically, each unit increase in NPAR independently increased PD odds by 8% (OR=1.08), and a linear dose-response relationship was observed, with PD risk increasing above approximately 13.95. Furthermore, within the PD cohort, higher NPAR levels were independently linked to increased all-cause mortality. Each unit increase in NPAR was associated with a 9% higher hazard of death (HR=1.09), with a linear association and increased mortality risk evident above NPAR levels of approximately 14.6. These findings collectively suggest that higher NPAR levels serve as an independent risk factor for both PD development and all-cause mortality in PD. This study significantly advances the understanding of systemic inflammation and nutritional status in PD by establishing, for the first time in a large, population-based cohort, a systematic link between NPAR and both PD risk and mortality. Previous research on peripheral inflammatory biomarkers in PD has primarily focused on single markers or other ratios such as the neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, or albumin-to-fibrinogen ratio[13,14,22]. While these markers have provided valuable insights into immune dysregulation and inflammation in PD, they often overlook the integrated impact of both acute phase response and overall nutritional/metabolic status. NPAR, in contrast, offers a more comprehensive and holistic assessment by combining two critical, yet opposing, indicators: the pro-inflammatory neutrophil percentage and the anti-inflammatory/nutritional marker albumin [23]. An elevation of NPAR, therefore, provides a composite signal of increased systemic inflammation compounded by potential nutritional compromise or systemic stress, reflecting a more severe biological perturbation. This integrated perspective is a key innovation of our study, moving beyond siloed examinations of immune cell ratios or isolated nutritional indicators for PD. Furthermore, unlike some inflammation markers that can fluctuate significantly due to acute infections, NPAR integrates a more stable long-term nutritional marker (albumin), potentially offering a more robust and sustained indicator of chronic systemic stress relevant to chronic neurodegenerative diseases like PD [24]. The observed association between elevated NPAR and increased PD risk likely involves complex interactions between systemic inflammation, nutritional status, and neurodegenerative processes. Some studies have pointed that neuroinflammation is a driver of dopaminergic neuronal degeneration in PD[25]. Neutrophils play a crucial role in host defense but can also induce neuroinflammation by releasing reactive oxygen species, proteases, and pro-inflammatory cytokines, potentially harming neurons or intensifying glial activation in the central nervous system (CNS)[26]. A higher peripheral neutrophil percentage may indicate a generalized inflammatory state affecting CNS vulnerability[27]. Albumin, primarily produced by the liver, serves multiple functions in addition to regulating osmotic pressure. It acts as a powerful antioxidant by neutralizing free radicals and binding metal ions like iron and copper, which can catalyze oxidative stress, a major factor in neuronal damage associated with PD[28,29]. Albumin may also offer immunomodulatory and neuroprotective benefits by interacting with neurotoxic substances or modulating inflammatory pathways[30]. A decreased albumin level, contributing to a higher NPAR, may indicate: 1) increased oxidative stress: lower albumin means less endogenous antioxidant capacity, leaving neurons more vulnerable to oxidative damage; 2) impaired nutrient delivery/metabolism: malnutrition or catabolic states leading to low albumin can deprive the brain of essential nutrients, impairing neuronal function and repair; 3) compromised detoxification: reduced albumin may lead to inefficient clearance of neurotoxins or drug metabolites[31]; 4) exacerbated inflammation: albumin has anti-inflammatory effects; its reduction could remove a natural brake on systemic and potentially neuroinflammation[32]. The combination of increased neutrophil-driven inflammation and reduced albumin-mediated neuroprotection/antioxidation, as captured by NPAR, presents a compelling synergistic mechanism that could accelerate neurodegeneration in PD. Recent studies indicate that chronic peripheral inflammation may weaken the BBB, permitting inflammatory mediators and immune cells to enter the CNS and exacerbate neuroinflammation [33,34,35]. Consequently, NPAR, indicative of systemic dysfunction, might serve as an indicator of BBB compromise and heightened brain susceptibility to peripheral inflammatory challenges. The linear positive association between NPAR and all-cause mortality in PD patients, particularly above the threshold of 14.6, warrants careful consideration. An excessively high NPAR (i.e., relatively high neutrophil percentage and low albumin) strongly suggests persistent systemic inflammatory responses, severe infections or poor nutritional status, which in PD patients can exacerbate neurodegeneration, increase the risk of infectious complications, and lead to worse clinical outcomes[38,39]. This finding underscores the importance of avoiding excessively high NPAR levels in PD patient management, offering clinicians a quantifiable prognostic marker for identifying high-risk individuals who may benefit from intensified monitoring or targeted interventions. Our findings bear significant clinical implications. Firstly, the identification of NPAR as a novel risk indicator for PD offers a readily accessible and cost-effective biomarker derived from routine blood tests. NPAR may have considerable potential for early risk stratification, particularly among those exhibiting non-motor symptoms or with elevated genetic susceptibility to PD. The linear positive association of NPAR with PD risk above a threshold of 13.95 further supports its utility as a potential screening marker in the general population. Moreover, among patients with established PD, the linear positive association of NPAR with all-cause mortality, especially above the threshold of 14.6, provides critical clinical insight, indicating that higher NPAR levels are associated with increased mortality risk. If validated in future prospective studies, these quantitative thresholds (13.95 for PD risk and 14.6 for mortality in PD patients) could provide clinicians with tangible markers for identifying individuals who might benefit from more intensive monitoring or targeted interventional strategies. For instance, interventions aiming to lower NPAR through anti-inflammatory or nutritional approaches in PD patients could potentially improve survival prognosis, though this requires rigorous clinical trial validation. Furthermore, NPAR could potentially serve as a prognostic marker to monitor disease progression or treatment response in established PD patients, similar to its utility in other chronic diseases. The strengths of this study encompass a large sample size, utilization of nationally representative NHANES data, robust statistical methods incorporating weighted analyses for complex survey designs, comprehensive adjustment for a wide range of potential confounders, and the application of RCS models to identify and visualize non-linear relationships. Notably, this study represents the first systematic exploration of the association between NPAR and both PD prevalence and all-cause mortality. However, several limitations should be acknowledged. First, the cross-sectional design inherently prevents establishing causal inferences between NPAR and PD prevalence. Future longitudinal cohort studies are imperative to ascertain the temporal relationship and confirm whether elevated NPAR contributes to PD development. Second, while NHANES data is generally reliable, PD diagnoses were primarily based on self-reported medication use, which could be affected by recall bias or misclassification. Third, despite extensive adjustments, residual confounding from unmeasured or imperfectly measured variables (e.g., specific dietary patterns, detailed non-PD medication profiles, genetic predispositions) cannot be entirely ruled out. Fourth, as the study focused solely on U.S. adults, further research is needed to determine if these findings apply to other ethnic groups or populations. Fifth, the absence of detailed clinical data precluded stratification of P cases into motor or non-motor subtypes, thereby limiting our ability to determine whether distinct PD phenotypes are associated with unique inflammatory and nutritional profiles[38]. Despite these limitations, we maintain that the large, representative cohort and robust analytical approach provide strong evidence for the association of NPAR with both PD risk and all-cause mortality among individuals with PD. This warrants further comprehensive investigation, rather than being considered merely a preliminary pilot study. Conclusion This study revealed a significant linear positive correlation between NPAR and PD prevalence in U.S. adults, NPAR levels, especially when exceeding the threshold of 13.95, serve as a significant risk factor for PD development in the general population. Moreover, among patients with established PD, an NPAR level above 14.6 significantly heightens the risk of all-cause mortality. These associations demonstrated robustness across various subgroups, without specific subgroups showing significantly different effects. These findings highlight the critical role of systemic inflammation and nutritional status in PD pathophysiology and prognosis. We propose that targeted interventions aiming to mitigate inflammation or improve nutritional status may represent a novel preventive or therapeutic strategy for high-risk populations and PD patients. Abbreviations AST, aspartate aminotransferase ALT, alanine aminotransferase BMI, body mass index CI, confidence intervals CNS, central nervous system DM, diabetes mellitus HR, hazard ratio NHANES, National Health and Nutrition Examination Survey NPAR, neutrophil percentage-to-albumin ratio OR, odds ratio PD, Parkinson’s disease RCS, restricted cubic spline Declarations Ethics approval and consent to participate The NHANES program, from which data for this study were obtained, operates under ethical guidelines initially established by the NHANES Institutional Review Board. This body was subsequently reformed as the National Center for Health Statistics Research Ethics Review Board. This investigation was conducted in strict accordance with the Declaration of Helsinki principles, pertinent local regulations, and the prevailing institutional protocols. Study protocols for NHANES were approved by the NHANES Institutional Review Board. All participants signed the informed consent before participating in the study. Consent for publication Not applicable Availability of data and materials The datasets used in this study are available in online repositories. Repository names and their accession numbers are available at https://www.cdc.gov/nchs/nhanes/ (accessed May 25, 2025). For any further inquiries, the corresponding author can be contacted directly. Competing interests The authors declare no competing interests. Funding The authors confirm that financial assistance was provided for the execution of this research and the subsequent publication of this article. A portion of this study received funding from the major project of the Science and Technology Department of Sichuan Province (Grant No. 2023YFS0164). Authors' contributions FJ.L. U. F. and J.C. conceived the idea and designed the study. SJ.D. ZY.L. and RB. Q. collected the data. FJ.L. U. F. and SJ.D. analyzed the data. FJ.L. U. F. and SJ.D. drafted the manuscript, and then RB. Q. ZY.L. and J.C. reviewed the manuscript. All authors read and approved the final draft. Acknowledgements We sincerely thank both the editor and the anonymous reviewers. Their insightful comments and diligent efforts were instrumental in significantly improving the quality and clarity of this manuscript. References Zhao N, Yang Y, Zhang L, Zhang Q, Balbuena L, Ungvari GS, et al. Quality of life in Parkinson’s disease: A systematic review and meta‐analysis of comparative studies. CNS Neurosci Ther. 2020;27:270–9. https://doi.org/10.1111/cns.13549. Wirdefeldt K, Adami H-O, Cole P, Trichopoulos D, Mandel J. Epidemiology and etiology of Parkinson’s disease: a review of the evidence. Eur J Epidemiol. 2011;26:1. https://doi.org/10.1007/s10654-011-9581-6. Feigin VL, Nichols E, Alam T, Bannick MS, Beghi E, Blake N, et al. 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Investigating the Diagnostic Significance of Neutrophil Percentage to Albumin Ratio (NPAR) in Patients with Infectious Meningitis. Journal of Health and Rehabilitation Research. 2024;4:897–904. https://doi.org/10.61919/jhrr.v4i2.969. Tansey MG, Wallings RL, Houser MC, Herrick MK, Keating CE, Joers V. Inflammation and immune dysfunction in Parkinson disease. Nat Rev Immunol. 2022;22:657–73. https://doi.org/10.1038/s41577-022-00684-6. Chu D, Gao J, Wang Z. Neutrophil-Mediated Delivery of Therapeutic Nanoparticles across Blood Vessel Barrier for Treatment of Inflammation and Infection. ACS Nano. 2015;9:11800–11. https://doi.org/10.1021/acsnano.5b05583. Lv X-N, Shen Y-Q, Li Z-Q, Deng L, Wang Z-J, Cheng J, et al. Neutrophil percentage to albumin ratio is associated with stroke-associated pneumonia and poor outcome in patients with spontaneous intracerebral hemorrhage. Front Immunol. 2023;14:1173718. https://doi.org/10.3389/fimmu.2023.1173718. Sun S, Wen Y, Li Y. Serum albumin, cognitive function, motor impairment, and survival prognosis in Parkinson disease. Medicine (Baltimore). 2022;101:e30324. https://doi.org/10.1097/MD.0000000000030324. Watanabe K, Kinoshita H, Okamoto T, Sugiura K, Kawashima S, Kimura T. Antioxidant Properties of Albumin and Diseases Related to Obstetrics and Gynecology. Antioxidants (Basel). 2025;14:55. https://doi.org/10.3390/antiox14010055. Prajapati KD, Sharma SS, Roy N. Current perspectives on potential role of albumin in neuroprotection. Rev Neurosci. 2011;22:355–63. https://doi.org/10.1515/RNS.2011.028. LeVine SM. Albumin and multiple sclerosis. BMC Neurol. 2016;16:47. https://doi.org/10.1186/s12883-016-0564-9. Gong Y, Li D, Cheng B, Ying B, Wang B. Increased neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with severe sepsis or septic shock. Epidemiol Infect. 148:e87. https://doi.org/10.1017/S0950268820000771. Zhang H, Wu T, Tian X, Lyu P, Wang J, Cao Y. High Neutrophil Percentage-To-Albumin Ratio Can Predict Occurrence of Stroke-Associated Infection. Front Neurol. 2021;12:705790. https://doi.org/10.3389/fneur.2021.705790. Brandl S, Reindl M. Blood–Brain Barrier Breakdown in Neuroinflammation: Current In Vitro Models. Int J Mol Sci. 2023;24:12699. https://doi.org/10.3390/ijms241612699. Pajares M, I. Rojo A, Manda G, Boscá L, Cuadrado A. Inflammation in Parkinson’s Disease: Mechanisms and Therapeutic Implications. Cells. 2020;9:1687. https://doi.org/10.3390/cells9071687. Berriat F, Lobsiger CS, Boillée S. The contribution of the peripheral immune system to neurodegeneration. Nat Neurosci. 2023;26:942–54. https://doi.org/10.1038/s41593-023-01323-6. Hosseini S, Shafiabadi N, Khanzadeh M, Ghaedi A, Ghorbanzadeh R, Azarhomayoun A, et al. Neutrophil to lymphocyte ratio in parkinson’s disease: a systematic review and meta-analysis. BMC Neurology. 2023;23:333. https://doi.org/10.1186/s12883-023-03380-7. Fereshtehnejad S-M, Zeighami Y, Dagher A, Postuma RB. Clinical criteria for subtyping Parkinson’s disease: biomarkers and longitudinal progression. Brain. 2017;140:1959–76. https://doi.org/10.1093/brain/awx118. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7311621","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511897801,"identity":"22338c65-891a-4b66-a6a5-565d4b362096","order_by":0,"name":"Fujun Liu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Fujun","middleName":"","lastName":"Liu","suffix":""},{"id":511897802,"identity":"6b1abf31-6ce7-4eca-bdee-0868f4a61f47","order_by":1,"name":"UMAR FAROOQ","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"UMAR","middleName":"","lastName":"FAROOQ","suffix":""},{"id":511897803,"identity":"22c7fc5b-f18c-4880-bd7b-6bf79d9a1188","order_by":2,"name":"Sijun Diao","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Sijun","middleName":"","lastName":"Diao","suffix":""},{"id":511897804,"identity":"5b104638-3de7-48e9-acd9-326b898dbc4a","order_by":3,"name":"Zhongyu Li","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhongyu","middleName":"","lastName":"Li","suffix":""},{"id":511897805,"identity":"4d9b67cb-f2ee-44a3-baa4-4e40bb854de8","order_by":4,"name":"Qibo Ran","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Qibo","middleName":"","lastName":"Ran","suffix":""},{"id":511897806,"identity":"fc2a02e1-8c1c-4eac-9d42-ade63406ddeb","order_by":5,"name":"Jing Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBACxmYg8QHCNiBeC+MMkrSAADMPSVqY23kPfrb5Y5fYwN68TYKh5g4xDuNLls5tS05s4DlWJsFw7BkxWnjMmHMbDiQ2SOSYSTA2HCZSi8UfoBb5N6RoYWAD2cJDvBZjyd62ZOM2nrRii4RjRGgx7D9j+OHHHzvZfvbDG298qCFGSwOUwQYiEghrYGCQJ0bRKBgFo2AUjHAAAM7HMfj74g6fAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-06 16:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7311621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7311621/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90982573,"identity":"d5b4d826-11c5-4d32-b6a0-4cdb81a7345c","added_by":"auto","created_at":"2025-09-10 09:30:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260132,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram illustrating the selection process of study participants. PD, Parkinson’s disease; NHANES, National Health and Nutrition Examination Survey; NPAR, neutrophil percentage-to-albumin ratio\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7311621/v1/75bc04f385b0b5dff3bf46eb.png"},{"id":90982575,"identity":"aca4f08f-aff3-4246-aae1-a8caf32ddb13","added_by":"auto","created_at":"2025-09-10 09:30:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187566,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curve for all-cause mortality in PD by NPAR tertile groups\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7311621/v1/118e82a00f85dcc80dd5da1a.png"},{"id":90982576,"identity":"09966242-a80c-46e4-b015-de927bab9c64","added_by":"auto","created_at":"2025-09-10 09:30:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":865973,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between NPAR and all-cause mortality in PD patients. No statistical differences between subgroups were observed (all p-values for interactions \u0026gt;0.05). BMI, body mass index; CI, confidence interval; PD, Parkinson's disease; DM, diabetes mellitus; NPAR, neutrophil percentage-to-albumin ratio\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7311621/v1/54ceff34df96015bd883d88b.png"},{"id":90982577,"identity":"a9a7bfff-69f3-4d26-8467-ecad6d472a19","added_by":"auto","created_at":"2025-09-10 09:30:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":455029,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline regression was conducted to examine the relationship between NPAR and both the risk of PD (Panel A) and all-cause mortality in PD patients (Panel B)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7311621/v1/8f19c97fd4c5327997581c6c.png"},{"id":99315127,"identity":"3674351c-1941-44e0-a418-a58218b1df2a","added_by":"auto","created_at":"2025-12-31 16:26:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2731970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7311621/v1/95dbccc9-c14b-45c5-a424-c88ba31a0929.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eNeutrophil percentage-to-albumin ratio increases the risk of Parkinson’s disease and all-cause mortality in Parkinson’s disease patients: A population-based Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eParkinson\u0026apos;s disease (PD) ,the second most prevalent neurodegenerative disorder, is characterized by motor symptoms such as bradykinesia, resting tremor, and rigidity, as well as non-motor manifestations including cognitive decline and sleep disturbances\u0026nbsp;[1,2]. The accelerating global aging of population has led to a notable increase in the incidence and prevalence of PD, with an estimated prevalence of approximately 4% in individuals aged 60 and above [3,4,5]. This escalating trend imposes a great challenge on both individuals and healthcare systems, highlighting the urgent need for effective strategies to identify high-risk populations early and enable timely interventions to improve patient outcomes and reduce socioeconomic burden.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;The pathological feature of PD is the progressive degeneration of dopamine-producing neurons within the substantia nigra pars of the midbrain, alongside the intracellular buildup of \u0026alpha;-synuclein and the subsequent formation of Lewy bodies in remaining dopaminergic neurons[6]. Recent research has highlighted the critical role of neuroinflammation in PD pathophysiology\u0026nbsp;[6]. However, the precise role of inflammation in neurodegeneration, specifically whether it serves as a primary cause or a consequence of neuronal damage, remains a subject of active debate and ongoing research[7,8].\u0026nbsp;\u0026nbsp;Nonetheless, inflammatory processes are believed to disrupt the blood-brain barrier, allowing immune cells to enter the central nervous system, leading to neuronal damage and worsening disease progression[9]. Furthermore, recent studies suggest that inflammation also plays a role in the development of atypical parkinsonism[10]. For instance, studies have indicated specific interleukin patterns in progressive supranuclear palsy, highlighting the diverse inflammatory landscapes across neurodegenerative conditions[10]. Consequently, peripheral blood inflammatory biomarkers have attracted considerable attention for their potential utility as diagnostic and prognostic indicators across a broad spectrum of medical conditions[11,12]. Previous studies on PD have investigated the diagnostic and prognostic significance of inflammation-related or nutritional markers, including neutrophil, lymphocyte, monocyte counts\u0026nbsp;[13], albumin-to-fibrinogen ratio[14], and systemic inflammation index\u0026nbsp;[11]. However, these existing markers often offer a limited, singular perspective on the complex interplay between systemic inflammation and nutritional status, failing to fully capture the dynamic interplay between immune activation and host metabolic/nutritional reserves.\u0026nbsp;The neutrophil percentage-to-albumin ratio (NPAR) is a prominent emerging composite biomarker that holistically reflects both systemic inflammatory burden and nutritional status[15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPAR is defined as the ratio of neutrophil percentage to serum albumin concentration[16]. Given the pivotal role of neutrophils in mediating inflammatory responses\u0026nbsp;and the close association of serum albumin levels with overall nutritional well-being and immune regulation, an elevated NPAR is suggested to indicate heightened systemic inflammation or impaired nutritional status. Unlike single inflammatory markers or simple ratios (e.g.,\u0026nbsp;lymphocyte, monocyte,\u0026nbsp;platelet-to-lymphocyte ratio) that capture only one facet of systemic response, NPAR\u0026apos;s unique advantage lies in its ability to simultaneously integrate two critical, yet often opposing, dimensions: the pro-inflammatory drive (neutrophil percentage) and the anti-inflammatory/nutritional protective capacity (albumin)[17]. This dual-dimensional information offers a more comprehensive and nuanced assessment of the overall systemic biological perturbation. Indeed, previous studies have shown that NPAR\u0026apos;s significant prognostic utility in conditions such as diabetes mellitus(DM), cardiovascular diseases, and various malignancies[17,\u0026nbsp;18,19].\u003c/p\u003e\n\u003cp\u003eTo our knowledge, the precise relationship between NPAR and the risk of PD prevalence and its association with all-cause mortality in PD patients are both unclear. Therefore, this study aims to systematically examine the relationship between NPAR and the risk of PD prevalence and all-cause mortality among PD patient, alongside the impact of different population characteristics on the all-cause mortality among PD patients in the 2001\u0026ndash;2018 National Health and Nutrition Examination Survey (NHANES) database. By comprehensively analyzing this biomarker, which is both readily accessible and cost-effective, this research seeks to provide a novel tool for early risk stratification and precision prevention and intervention of PD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics administers NHANES, a biennial cross-sectional survey, to assess U.S. population health by collecting data from randomly selected non-institutionalized individuals. The NHANES project was approved by the National Center for Health Statistics Institutional Review Board, and all participants provided written informed consent. This study used publicly accessible, de-identified NHANES data, eliminating the need for further local ethics committee approval according to our institutional guidelines. The study adhered to the STROBE Statement guidelines for observational research reporting. Data for this study were obtained from eight consecutive two-year cycles of NHANES, spanning from 2001 to 2018. The study initially included 91,351 participants. Participants under 40 years old (n=59,182) were excluded to maintain clinical relevance for PD research, leaving 32,169 individuals. Subsequently, participants with missing follow-up data (n=1570), unavailable NPAR data (n=1992), and unavailable PD data (n=33) were excluded, totaling 3,595 exclusions and leaving 28,574 individuals. Furthermore, those with missing data for other key covariates (n= 3,404) were excluded. This rigorous selection process yielded a final analytical sample of 25,170 eligible participants for this study. \u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1\u003c/strong\u003e illustrates the participant selection process in detail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Definitions of exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary exposure variable in this study was the NPAR. NPAR is a composite biomarker derived from the percentage of neutrophils in the blood and the serum albumin concentration. NPAR is defined as the ratio of neutrophil percentage to serum albumin concentration (expressed in g/dL)[16]. Neutrophil percentage was measured using the Beckman Coulter Automated Hematology Analyzer DxH 900, adhering to the standardized NHANES laboratory protocol. Serum albumin concentration was determined using the biochromatic digital endpoint method on the LX20 analyzer. Study subjects were stratified into tertiles according to NPAR thresholds: Q1 (\u0026lt;=12.9), Q2 (12.9,15], and Q3 (\u0026gt;\u0026thinsp;15), utilizing Q1 as the comparator cohort.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study selected and incorporated covariates for statistical adjustment based on existing literature and clinical relevance to effectively control for potential confounding. These covariates encompassed multiple dimensions: demographic factors included such as age, sex (male/female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Race), education level (three categories), marital status (two categories), and poverty income ratio(PIR) (two categories); behavioral factors primarily comprised smoking status (yes/no); clinical factors involved body mass index (three categories), history of stroke, DM, coronary heart disease, hyperlipidemia, and hypertension (all defined as yes/no); and laboratory factors included red blood cell count and serum creatinine (both continuous). Notably, to avoid collinearity with NPAR, which is the primary exposure variable, its constituent components\u0026mdash;neutrophil percentage and serum albumin\u0026mdash;were not included as separate covariates in the multivariable models. Covariate data were sourced from questionnaires, physical exams, and lab measurements in the NHANES database. Comprehensive details are available on the NHANES website (www.cdc.gov/nchs/nhanes).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDetermination of PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePD was identified through participants\u0026apos; self-reported use of anti-Parkinson medications. Participants were identified as having PD if they reported current use of any medication under the \u0026quot;Anti-Parkinson Agents\u0026quot; category within the NHANES Prescription Medications data. These drug categories include, but are not limited to, levodopa, carbidopa, entacapone, pramipexole, and amantadine[20]. Individuals not reporting the use of anti-Parkinson medications were classified as non-PD. This operational definition of PD, based on self-reported anti-Parkinson medication use, aligns with established methods from previous NHANES-based studies. This approach is widely accepted as it effectively addresses the limitation of lacking clinical PD diagnoses in NHANES data and is considered a valid strategy for PD-related research using this database\u0026nbsp;[11,21].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFollow-up and outcome assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome measures were the prevalence of PD and all-cause mortality in PD patients. Participant vital status was confirmed using the National Death Index. Participant status follow-up data in the NHANES database were systematically updated monthly. The collected follow-up data included participants\u0026apos; survival status at each assessment, survival duration, causes of death, and other pertinent demographic and health information. Causes of death were classified into established categories, including traffic accidents, heart disease, pneumonia and influenza, kidney disease, chronic respiratory diseases, cerebrovascular accidents, DM, malignant neoplasms, and other causes. All-cause mortality refers to any death from any cause during the observation period.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s statistical analyses incorporated NHANES\u0026apos;s complex sampling design and applied weighting to analyses. Continuous variables are presented as weighted means with standard deviations, and categorical variables as weighted percentages. Weighted independent samples t-tests and weighted chi-square tests were employed to compare baseline characteristics between groups for continuous and categorical variables, respectively. We employed weighted multivariate logistic regression models to assess the association between NPAR and PD prevalence, presenting adjusted odds ratios (OR) with 95% confidence intervals (CI) to quantify the strength and significance of the relationship. In the regression analyses, NPAR was analyzed both continuously and categorically, using its quartiles within the study population. Several models were constructed to progressively adjust for potential confounding factors. After establishing the relationship between NPAR and the prevalence of PD, we selected the PD cohort and further examined the association between NPAR and all-cause mortality using multivariate Cox regression. The hazard ratio (HR) served as the primary measure to quantify this relationship with mortality, where an HR value below 1 indicated a protective influence, and an HR exceeding 1 suggested an increased risk. For the multivariate Cox regression analysis, three distinct models were employed to meticulously address and adjust for potential confounding effects. Kaplan\u0026ndash;Meier survival curves were generated to compare survival rates among different PD groups (Q1, Q2 and Q3) using the Log rank test. \u0026nbsp;Finally, the quantitative assessment of the relationship between NPAR and clinical outcomes was performed via restricted cubic spline (RCS) analysis. RCS curves were generated to pinpoint precise NPAR levels that exhibited significant associations with adverse outcomes. Subgroup analyses were performed to evaluate if the association between NPAR and mortality among PD patients remains consistent across various subgroups. The models incorporated interaction terms to formally assess effect modification. Statistical analyses were conducted using R software (version 4.4.2). A two-sided p-value below 0.05 was set as the criterion for statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic and clinical features of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final study included 25,170 participants, 309 of whom were diagnosed with PD. Participants were categorized into PD and non-PD groups according to the established screening criteria for PD. Weighted demographic, lifestyle, and biomarker characteristics are presented in\u003cstrong\u003e\u0026nbsp;Table 1.\u003c/strong\u003e PD patients were significantly older than those non-PD patients. A borderline significant difference was observed in sex distribution, with a higher proportion of females in the PD group (59.86%) compared to the non-PD group (52.35%). Significant differences were also noted in ethnicity and PIR, with White individuals and those in the lowest income bracket being more prevalent in the PD cohort. Regarding lifestyle and health conditions, PD patients exhibited a significantly higher prevalence of stroke, DM, and hypertension. BMI also differed significantly, with a higher proportion of PD patients categorized as obese. In terms of laboratory tests, neutrophil percentage, and creatinine were significantly higher in the PD group, while albumin and red blood cell count were significantly lower. No significant differences were observed for marital status, education level, smoking status, hyperlipidemia, coronary heart disease, ALT, AST, globulin, phosphorus, uric acid, sodium, and total calcium. Interestingly, NPAR levels were significantly higher in the PD group than in the non-PD group, indicating that NPAR might serve as a risk factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Weighted characteristics of study participants from the NHANES, 2001-2018.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Parkinson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParkinson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24861 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e309\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e57.80\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e57.76\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e61.32\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12745(52.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12588(52.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e157(59.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12425(47.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12273(47.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e152(40.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eMarital status, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e15771(68.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e15600(68.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e171(62.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; unmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9399(31.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e9261(31.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e138(37.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eEducation level, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Less than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3402(6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3364(6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e38(6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9331(34.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e9210(34.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e121(36.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; More than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12437(59.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12287(59.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e150(56.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eEthnicity, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12212(74.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12013(74.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e199(81.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3709(5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3675(5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e34(4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5074(9.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e5031(9.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e43(7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4175(10.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4142(10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e33(6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003ePoverty income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8345(20.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e8214(20.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e131(30.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e16825(79.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e16647(79.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e178(69.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12632(50.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12485(50.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e147(52.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e12538(49.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12376(49.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e162(47.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e6448(26.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e6374(26.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e74(26.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 25-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e8975(35.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e8891(35.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e84(25.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9747(38.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e9596(38.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e151(47.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e23765(95.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e23497(95.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e268(87.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1405(4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1364(4.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e41(12.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e5029(19.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4972(19.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e57(18.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e20141(80.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e19889(80.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e252(81.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e20463(85.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20236(85.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e227(80.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4707(14.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4625(14.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e82(19.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e23544(94.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e23269(94.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e275(91.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1626(5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1592(5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e34(8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1102(4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1095(4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7(1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e24068(95.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e23766(95.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e302(98.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eNPAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e14.01\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e14.01\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14.78\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eNeutrophil percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e58.78\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e58.75\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e60.66\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.22\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.22\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4.13\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eRed blood cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.66\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e4.66\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4.56\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eAlt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25.09\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.10\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e24.30\u0026plusmn;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eAst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e25.63\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.62\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e26.61\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eGlobulin_g.dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2.85\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.85\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.82\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eCreatinine_mg.dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.92\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.97\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003ePhosphorus_mmol.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eUric_acid_umol.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e326.62\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e326.55\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e332.28\u0026plusmn;6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eSodium_mmol.L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e139.30\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e139.30\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e139.20\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 190px;\"\u003e\n \u003cp\u003eCalcium_total_mg.dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e9.42\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e9.42\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9.38\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAST, aspartate aminotransferase; ALT, alanine aminotransferase; BMI, body mass index;\u0026nbsp;DM;\u0026nbsp;No., number; NPAR: neutrophil percentage-to-albumin ratio; OR, odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe association between NPAR and prevalence of PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed weighted multivariate logistic regression analysis to determine the relationship between NPAR and PD prevalence, calculating the OR and 95% CI (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u0026nbsp;Both NPAR as a continuous variable and as quartile were assessed across four models: the Crude Model (unadjusted), Model 1 (adjusted for demographics), and Model 2 (adjusted for all covariates).\u003c/p\u003e\n\u003cp\u003eAs a continuous variable, NPAR was consistently and significantly associated with an increased prevalence of PD across all models. In the Crude Model, each unit increase in NPAR was associated with 12% increased odds of PD (OR = 1.12, 95% CI: 1.06-1.19, p \u0026lt; 0.001). This association remained statistically significant, albeit with slightly attenuated odds ratios, after progressive adjustment for covariates: Model 1 (OR = 1.10, 95% CI: 1.03-1.17, p = 0.003), and Model 2 (OR = 1.08, 95% CI: 1.02-1.15, p = 0.01). When NPAR was categorized into quartiles, with the Q1 as the reference, the Q3 consistently demonstrated a significantly increased odds of PD across all models. Specifically, the odds ratios for Q3 were: 1.94 (95% CI: 1.30-2.91, p = 0.001) in the Crude Model, 1.72 (95% CI: 1.13-2.61, p = 0.012) in Model 1, and 1.54 (95% CI: 1.02-2.34, p = 0.042) in Model 2. Importantly, a significant p for trend was observed across all models (p \u0026lt; 0.05), indicating a dose-response relationship where higher NPAR quartiles were associated with an increased risk of PD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 2.\u0026nbsp;\u003c/strong\u003eMultivariate weighted logistic regression analysis of the association between NPAR and the prevalence of PD\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPAR~Parkinson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude Model*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCharacter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003eOR 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eOR 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eOR 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.12(1.06,1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.10(1.03,1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.08(1.02,1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003eNPAR quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.42(0.92,2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.35(0.87,2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.32(0.86,2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.94(1.30,2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.72(1.13,2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.54(1.02,2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003ep for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNPAR: neutrophil percentage-to-albumin ratio. The crude model* is devoid of covariates. Model 1* contains only age, education, sex, ethnicity, marital status, and poverty income ratio. Model 2* incorporates all covariates.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePositive association of NPAR with all-cause mortality in PD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine if NPAR continues to serve as a risk factor for individuals already diagnosed with PD, we conducted survival analyses employing cox regression models and Kaplan-Meier curves. Cox regression analysis (\u003cstrong\u003eTable 3\u003c/strong\u003e) examined the association between NPAR, both as a continuous variable and categorized into tertile groups (Q1, Q2, Q3, with Q1 serving as the reference), and all-cause mortality. We constructed three sequential models: Model 4 (unadjusted), Model 5 (partially adjusted), and Model 6 (fully adjusted for all covariates considered in this study).\u003c/p\u003e\n\u003cp\u003eAs a continuous variable, NPAR was consistently and significantly associated with an increased risk of all-cause mortality in PD patients across all models. In the Model 4, each unit increase in NPAR was associated with a 14% increased hazard of all-cause mortality (HR = 1.14, 95% CI: 1.07\u0026ndash;1.22, p \u0026lt; 0.001). This association remained statistically significant, albeit with slightly attenuated hazard ratios, after progressive adjustment for covariates: Model 5(HR = 1.12, 95% CI: 1.04\u0026ndash;1.20, p = 0.002), and Model 6 (HR = 1.09, 95% CI: 1.02\u0026ndash;1.17, p = 0.015)\u003c/p\u003e\n\u003cp\u003eWhen NPAR was categorized into tertiles, with Q1 as the reference, the highest Q3 consistently demonstrated a significantly increased risk of all-cause mortality in PD across all models. Specifically, the hazard ratios for Q3 were: 1.87 (95% CI: 1.11\u0026ndash;3.15, p=0.019) in Model 4, 1.73 (95% CI: 1.01\u0026ndash;2.98, p=0.046) in Model 5, and 1.85 (95% CI: 1.03\u0026ndash;3.33, p=0.040) in Model 6. In contrast, the middle tertile (Q2) did not show a statistically significant association with all-cause mortality in any model (HR=1.44, 95% CI: 0.82\u0026ndash;2.53, p=0.203 in Model 4; HR=1.66, 95% CI: 0.92\u0026ndash;3.01, p=0.093 in Model 5; HR=1.77, 95% CI: 0.94\u0026ndash;3.39, p=0.076 in Model 6), suggesting the effect is primarily driven by the highest exposure group. Kaplan\u0026ndash;Meier analysis showed, in the PD cohort, the highest survival rate in Q1 group, with significant differences noted (p=\u0026thinsp;0.042)\u003cstrong\u003e\u0026nbsp;(Fig. 2)\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIn contrast, those in the highest NPAR tertile (Q3, represented by the blue line) consistently showed the lowest survival probability. The survival curve for the middle NPAR tertile (Q2) fell between Q1 and Q3, generally tracking closer to Q3, particularly in the later stages of the observation period. These findings collectively indicate that higher NPAR levels are associated with reduced survival probabilities in the studied cohort.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u0026nbsp;Table 3.\u003c/strong\u003e Multivariate cox regression analysis of NPAR and All-cause mortality in PD patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.07, 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.82, 2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.11, 3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 5*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.04, 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.92, 3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.01, 2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 6*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.02, 1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.94, 3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 183px;\"\u003e\n \u003cp\u003e1.03, 3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Model4*:no confounding factors were adjusted. Model 5* contains only age, education, sex, ethnicity, marital status, and poverty income ratio. Model 6* incorporates all covariates in this study. HR, Hazard Ratio; CI, Confidence Interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis and interaction analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter establishing NPAR as a risk factor for all-cause mortality, we further conducted subgroup and interaction analyses to evaluate the robustness of this association and potential effect modification in the PD cohort (\u003cstrong\u003eFig.3\u003c/strong\u003e). Subgroups were defined by age, gender, ethnicity, marital status, education level, PIR, BMI, history of stroke, hyperlipidemia, DM, coronary heart disease, and smoking status. The subgroup analysis demonstrated that higher NPAR levels were significantly associated with increased all-cause mortality in participants aged \u0026ge;65 years (p=0.040), those of Mexican American ethnicity (p=0.042), those with a PIR \u0026lt;1.5 (p=0.041), those without a history of stroke (p=0.037), those with hyperlipidemia (p=0.031), those without DM (p=0.041), and non-smokers (p=0.026). No statistically significant associations (p\u0026lt; 0.05) were observed in the corresponding complementary subgroups, nor in the subgroups of White ethnicity (p=0.053), married individuals (p=0.054), or individuals without coronary heart disease (p=0.054). Similarly, no significant associations were found when stratifying by gender, education level, or BMI. Interaction analyses showed that NPAR was an independent factor influencing all-cause mortality, with no statistically significant interactions for all variables (all p- interaction \u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative evaluation of NPAR Levels and Adverse Outcomes via RCS regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further explored the quantitative relationships between NPAR and adverse outcomes using RCS regression analysis, as presented in \u003cstrong\u003eFig 4\u003c/strong\u003e. Panel A illustrates the dose-response relationship between NPAR and the risk of PD in\u0026nbsp;the general population. The overall p-value for the association was statistically significant (p-overall = 0.0217). However, the test for non-linearity was not significant (NL-p value = 0.8598), indicating a linear relationship. The adjusted OR for PD risk was observed to increase linearly with higher NPAR values. Specifically, the odds of developing PD progressively increased when NPAR values surpassed approximately 13.95.\u003c/p\u003e\n\u003cp\u003ePanel B depicts the dose-response relationship between NPAR and all-cause mortality in PD patients. A significant overall association was also identified (p-overall = 0.0145). Similar to Panel A, the non-linearity test was not significant (NL-p value = 0.1835), confirming a linear relationship. The adjusted HR for all-cause mortality in PD linearly increased with rising NPAR levels. An increased risk of all-cause mortality became evident when NPAR values exceeded approximately 14.6. These quantitative analyses demonstrate that higher NPAR levels are linearly and significantly associated with an elevated risk of both PD incidence and all-cause mortality in PD patients. The findings suggest that maintaining NPAR levels below approximately 13.95 for PD risk and 14.6 for all-cause mortality may be beneficial.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis population-based study firstly systematically examined the association between NPAR and both PD prevalence and all-cause mortality in PD patients using NHANES 2001-2018 data.\u0026nbsp;Our findings demonstrate that higher NPAR levels are significantly associated with an increased prevalence of PD in the general population. Specifically, each unit increase in NPAR independently increased PD odds by 8% (OR=1.08), and a linear dose-response relationship was observed, with PD risk increasing above approximately 13.95. Furthermore, within the PD cohort, higher NPAR levels were independently linked to increased all-cause mortality. Each unit increase in NPAR was associated with a 9% higher hazard of death (HR=1.09), with a linear association and increased mortality risk evident above NPAR levels of approximately 14.6. These findings collectively suggest that higher NPAR levels serve as an independent risk factor for both PD development and all-cause mortality in PD.\u003c/p\u003e\n\u003cp\u003eThis study significantly advances the understanding of systemic inflammation and nutritional status in PD by establishing, for the first time in a large, population-based cohort, a systematic link between NPAR and both PD risk and mortality. Previous research on peripheral inflammatory biomarkers in PD has primarily focused on single markers or other ratios such as the neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, or\u0026nbsp;albumin-to-fibrinogen ratio[13,14,22]. While these markers have provided valuable insights into immune dysregulation and inflammation in PD, they often overlook the integrated impact of both acute phase response and overall nutritional/metabolic status. NPAR, in contrast, offers a more comprehensive and holistic assessment by combining two critical, yet opposing, indicators: the pro-inflammatory neutrophil percentage and the anti-inflammatory/nutritional marker albumin\u0026nbsp;[23]. An elevation of NPAR, therefore, provides a composite signal of increased systemic inflammation compounded by potential nutritional compromise or systemic stress, reflecting a more severe biological perturbation. This integrated perspective is a key innovation of our study, moving beyond siloed examinations of immune cell ratios or isolated nutritional indicators for PD. Furthermore, unlike some inflammation markers that can fluctuate significantly due to acute infections, NPAR integrates a more stable long-term nutritional marker (albumin), potentially offering a more robust and sustained indicator of chronic systemic stress relevant to chronic neurodegenerative diseases like PD\u0026nbsp;[24].\u003c/p\u003e\n\u003cp\u003eThe observed association between elevated NPAR and increased PD risk likely involves complex interactions between systemic inflammation, nutritional status, and neurodegenerative processes. Some studies have pointed that neuroinflammation is a driver of dopaminergic neuronal degeneration in PD[25]. Neutrophils play a crucial role in host defense but can also induce neuroinflammation by releasing reactive oxygen species, proteases, and pro-inflammatory cytokines, potentially harming neurons or intensifying glial activation in the central nervous system (CNS)[26]. A higher peripheral neutrophil percentage may indicate a generalized inflammatory state affecting CNS vulnerability[27]. Albumin, primarily produced by the liver, serves multiple functions in addition to regulating osmotic pressure.\u0026nbsp;It acts as a powerful antioxidant by \u0026nbsp;neutralizing free radicals and binding metal ions like iron and copper, \u0026nbsp;which can catalyze oxidative stress, a major factor in neuronal damage associated with\u0026nbsp;PD[28,29]. Albumin may also offer immunomodulatory and neuroprotective benefits by interacting with neurotoxic substances or modulating inflammatory pathways[30]. A decreased albumin level, contributing to a higher NPAR, may indicate: 1) increased oxidative stress: lower albumin means less endogenous antioxidant capacity, leaving neurons more vulnerable to oxidative damage; 2) impaired nutrient delivery/metabolism: malnutrition or catabolic states leading to low albumin can deprive the brain of essential nutrients, impairing neuronal function and repair; 3) compromised detoxification: reduced albumin may lead to inefficient clearance of neurotoxins or drug metabolites[31]; 4) exacerbated inflammation: albumin has anti-inflammatory effects; its reduction could remove a natural brake on systemic and potentially neuroinflammation[32]. The combination of increased neutrophil-driven inflammation and reduced albumin-mediated neuroprotection/antioxidation, as captured by NPAR, presents a compelling synergistic mechanism that could accelerate neurodegeneration in PD. Recent studies indicate that chronic peripheral inflammation may weaken the BBB, permitting inflammatory mediators and immune cells to enter the CNS and exacerbate neuroinflammation\u0026nbsp;[33,34,35]. Consequently, NPAR, indicative of systemic dysfunction, might serve as an indicator of BBB compromise and heightened brain susceptibility to peripheral inflammatory challenges.\u003c/p\u003e\n\u003cp\u003eThe linear positive association between NPAR and all-cause mortality in PD patients, particularly above the threshold of 14.6, warrants careful consideration. An excessively high NPAR (i.e., relatively high neutrophil percentage and low albumin) strongly suggests persistent systemic inflammatory responses, severe infections or poor nutritional status, which in PD patients can exacerbate neurodegeneration, increase the risk of infectious complications, and lead to worse clinical outcomes[38,39]. This finding underscores the importance of avoiding excessively high NPAR levels in PD patient management, offering clinicians a quantifiable prognostic marker for identifying high-risk individuals who may benefit from intensified monitoring or targeted interventions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Our findings bear significant clinical implications. Firstly, the identification of NPAR as a novel risk indicator for PD offers a readily accessible and cost-effective biomarker derived from routine blood tests. NPAR may have considerable potential for early risk stratification, particularly among those exhibiting non-motor symptoms or with elevated genetic susceptibility to PD. The linear positive association of NPAR with PD risk above a threshold of 13.95 further supports its utility as a potential screening marker in the general population. Moreover, among patients with established PD, the linear positive association of NPAR with all-cause mortality, especially above the threshold of 14.6, provides critical clinical insight, indicating that higher NPAR levels are associated with increased mortality risk. If validated in future prospective studies, these quantitative thresholds (13.95 for PD risk and 14.6 for mortality in PD patients) could provide clinicians with tangible markers for identifying individuals who might benefit from more intensive monitoring or targeted interventional strategies. For instance, interventions aiming to lower NPAR through anti-inflammatory or nutritional approaches in PD patients could potentially improve survival prognosis, though this requires rigorous clinical trial validation. Furthermore, NPAR could potentially serve as a prognostic marker to monitor disease progression or treatment response in established PD patients, similar to its utility in other chronic diseases.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;The strengths of this study encompass a large sample size, utilization of nationally representative NHANES data, robust statistical methods incorporating weighted analyses for complex survey designs, comprehensive adjustment for a wide range of potential confounders, and the application of RCS models to identify and visualize non-linear relationships. Notably, this study represents the first systematic exploration of the association between NPAR and both PD prevalence and all-cause mortality. However, several limitations should be acknowledged. First, the cross-sectional design inherently prevents establishing causal inferences between NPAR and PD prevalence. Future longitudinal cohort studies are imperative to ascertain the temporal relationship and confirm whether elevated NPAR contributes to PD development. Second, while NHANES data is generally reliable, PD diagnoses were primarily based on self-reported medication use, which could be affected by recall bias or misclassification. Third, despite extensive adjustments, residual confounding from unmeasured or imperfectly measured variables (e.g., specific dietary patterns, detailed non-PD medication profiles, genetic predispositions) cannot be entirely ruled out. Fourth, as the study focused solely on U.S. adults, further research is needed to determine if these findings apply to other ethnic groups or populations.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFifth, the absence of detailed clinical data precluded stratification of P cases into motor or non-motor subtypes, thereby limiting our ability to determine whether distinct PD phenotypes are associated with unique inflammatory and nutritional profiles[38].\u0026nbsp;\u0026nbsp;Despite these limitations, we maintain that the large, representative cohort and robust analytical approach provide strong evidence for the association of NPAR with both\u0026nbsp;PD\u0026nbsp;risk and all-cause mortality among individuals with PD. This warrants further comprehensive investigation, rather than being considered merely a preliminary pilot study.\u003c/p\u003e\n"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed a significant linear positive correlation between NPAR and PD prevalence in U.S. adults, NPAR levels, especially when exceeding the threshold of 13.95, serve as a significant risk factor for PD development in the general population. Moreover, among patients with established PD, an NPAR level above 14.6 significantly heightens the risk of all-cause mortality. These associations demonstrated robustness across various subgroups, without specific subgroups showing significantly different effects. These findings highlight the critical role of systemic inflammation and nutritional status in PD pathophysiology and prognosis. We propose that targeted interventions aiming to mitigate inflammation or improve nutritional status may represent a novel preventive or therapeutic strategy for high-risk populations and PD patients.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eAST,\u0026nbsp;aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eALT,\u0026nbsp;alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eBMI,\u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eCI,\u0026nbsp;confidence intervals\u003c/p\u003e\n\u003cp\u003eCNS,\u0026nbsp;central nervous system\u003c/p\u003e\n\u003cp\u003eDM,\u0026nbsp;diabetes mellitus\u003c/p\u003e\n\u003cp\u003eHR,\u0026nbsp;hazard ratio\u003c/p\u003e\n\u003cp\u003eNHANES,\u0026nbsp;National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eNPAR,\u0026nbsp;neutrophil percentage-to-albumin ratio\u003c/p\u003e\n\u003cp\u003eOR,\u0026nbsp;odds ratio\u003c/p\u003e\n\u003cp\u003ePD,\u0026nbsp;Parkinson\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eRCS, restricted cubic spline\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES program, from which data for this study were obtained, operates under ethical guidelines initially established by the NHANES Institutional Review Board. This body was subsequently reformed as the\u0026nbsp;National Center for Health Statistics\u0026nbsp;Research Ethics Review Board. This investigation was conducted in strict accordance with the Declaration of Helsinki principles, pertinent local regulations, and the prevailing institutional\u0026nbsp;protocols.\u0026nbsp;Study protocols for NHANES were approved by the NHANES Institutional Review Board. All participants signed the informed consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are available in online repositories.\u0026nbsp;Repository names and their accession numbers are available at https://www.cdc.gov/nchs/nhanes/ (accessed May 25, 2025).\u0026nbsp;For any further inquiries, the corresponding author can be contacted directly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that financial assistance was provided for the execution of this research and the subsequent publication of this article. A portion of this study received funding from the major project of the Science and Technology Department of Sichuan Province (Grant No. 2023YFS0164).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFJ.L. U. F. and J.C. conceived the idea and designed the study. SJ.D. ZY.L. and RB. Q. collected the data. FJ.L. U. F. and SJ.D. analyzed the data. FJ.L. U. F. and SJ.D. drafted the manuscript, and then RB. Q. ZY.L. and J.C. reviewed the manuscript. All authors read and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank both the editor and the anonymous reviewers. Their insightful comments and diligent efforts were instrumental in significantly improving the quality and clarity of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao N, Yang Y, Zhang L, Zhang Q, Balbuena L, Ungvari GS, et al. Quality of life in Parkinson\u0026rsquo;s disease: A systematic review and meta‐analysis of comparative studies. CNS Neurosci Ther. 2020;27:270\u0026ndash;9. https://doi.org/10.1111/cns.13549.\u003c/li\u003e\n\u003cli\u003eWirdefeldt K, Adami H-O, Cole P, Trichopoulos D, Mandel J. Epidemiology and etiology of Parkinson\u0026rsquo;s disease: a review of the evidence. Eur J Epidemiol. 2011;26:1. https://doi.org/10.1007/s10654-011-9581-6.\u003c/li\u003e\n\u003cli\u003eFeigin VL, Nichols E, Alam T, Bannick MS, Beghi E, Blake N, et al. Global, regional, and national burden of neurological disorders, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. 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Brain. 2017;140:1959\u0026ndash;76. https://doi.org/10.1093/brain/awx118.\u003c/li\u003e\n\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, National Health and Nutrition Examination Survey, Neutrophil percentage-to-albumin ratio, NPAR, mortality, risk","lastPublishedDoi":"10.21203/rs.3.rs-7311621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: This study aims to investigate the association of neutrophil percentage-to-albumin ratio (NPAR) with Parkinson’s disease (PD) prevalence and all-cause mortality risk in PD patients.\u003c/p\u003e\n\u003cp\u003eMethods: Using NHANES 2001-2018 data (n=25,170; 309 PD patients), weighted multivariate logistic regression and Cox regression analyses were applied to evaluate the associations between NPAR and the prevalence of PD, as well as between NPAR and all-cause mortality risk. Restricted cubic spline (RCS) analysis elucidated the precise relationships. Consistency of results was checked through subgroup analysis.\u003c/p\u003e\n\u003cp\u003eResults: The mean NPAR was higher in PD (14.78±0.21) vs. that in non-PD (14.01±0.03; p \u0026lt; 0.001). After adjusting all variables, each unit increase in NPAR corresponded to 8% higher PD odds (OR=1.08, 95%CI:1.02-1.15, p=0.01). The prevalence of PD in the highest quintile (Q3) was 1.54 times higher than that in the lowest quintile (Q1) (OR=1.54; 95% CI, 1.02-2.34, p=0.042). The RCS analysis confirmed a linear dose-response relationship (non-linear p=0.8598), with PD risk increasing progressively at NPAR levels above approximately 13.95 (P-overall=0.0217). For all-cause mortality in PD patients, NPAR was also significantly associated. \u0026nbsp;In the fully adjusted model, each unit increase in NPAR was linked to a 9% higher hazard of death (HR=1.09, 95%CI: 1.02–1.17, p=0.015), and patients with highest Q3 had an 85% higher mortality risk compared to the Q1 (HR=1.85, 95% CI: 1.03–3.33, p=0.040). Furthermore, Kaplan-Meier analysis demonstrated that PD patients in the Q1 exhibited the highest survival rates (Log-rank test, p=0.042). RCS analysis revealed a linear association (non-linear p=0.1835) for mortality, with increased risk evident above NPAR levels of approximately 14.6 (p-overall=0.0145). Subgroup analyses and interaction tests demonstrated the robustness of this association (p-interaction\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eNPAR exhibits a linear, positive association with PD risk in the general population and with all-cause mortality in PD patients. Higher NPAR levels, particularly above 13.95 for PD risk and 14.6 for all-cause mortality, are associated with increased risk. These findings highlight the potential of NPAR as a biomarker for risk stratification and prognosis in PD.\u003c/p\u003e","manuscriptTitle":"Neutrophil percentage-to-albumin ratio increases the risk of Parkinson’s disease and all-cause mortality in Parkinson’s disease patients: A population-based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-10 09:29:58","doi":"10.21203/rs.3.rs-7311621/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":"354f2c49-cb30-4b7a-a83c-15817f69ce56","owner":[],"postedDate":"September 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-27T11:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-10 09:29:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7311621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311621","identity":"rs-7311621","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}