Association between serum transferrin receptor and Parkinson's disease: A large cross-sectional study from NHANES

Association between serum transferrin receptor and Parkinson's disease: A large cross-sectional study from NHANES | 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 Association between serum transferrin receptor and Parkinson's disease: A large cross-sectional study from NHANES Yan Su, Sheng Cai, Yang Xu, Xianwen Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7672096/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Parkinson's disease(PD) is a progressive neurodegenerative diorders with a rising incidence. This study aims to investigate the relationship between iron metabolism biomarkers and the risk of PD. Methods A cross-sectional study was performed on 4,496 adults using National Health and Nutrition Examination Survey (NHANES) data (2005–2010, 2015–2018), including 4,496 adult participants. The logistic regression analysis was employed to detect the relationship between serum transferrin receptor(TFR) and PD. In addition, smooth curve fitting method was applied to evaluate the dose-response relationship. Sensitivity analysis, subgroup analysis, and intergroup interaction tests were conducted to evaluate the robustness of the relationship. Results Among the 4496 participants, 74 were diagnosed with PD, while 4,422 were non-PD. In both unadjusted and adjusted covariate models, multivariate logistic regression revealed a significant positive correlation between the elevated TFR and PD risk (P < 0.05). Smooth curve fitting analysis indicated gradual increase in PD risk with rising TFR concentrations.Subgroup analyses and interaction tests demonstrated that this association remained consistent regardless of BMI, hypertension, dyslipidemia, diabetes, smoke status, drink, and PA (all interaction P > 0.05). Sensitivity analysis further supported the correlation. Conclusion Higher serum TFR levels are independently correlated with increased risk of PD,suggesting a potential role in early PD prediction.Conversely, maintaining lower TFR level might reduce the risk of PD onset.These finding highlights the importance of iron metabolism in PD pathogenesis and deserve further longitudinal investigation. serum iron ferritin transferrin receptor Parkinson's disease NHANES database cross-sectional study Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson's disease (PD),ranking as the second most prevalent neurodegenerative disorder globally, currently impacts over 6 million individuals globally[ 1 ]. Epidemiological projections indicate a doubling of PD patients to exceed 12 million by 2040, primarily caused by population aging trends[ 2 ][ 3 ].Clinically, PD manifests through a spectrum of motor dysfunction (e.g., resting tremor, bradykinesia) and non-motor features including sleep fragmentation and cognitive decline [ 4 ],These multisystem impairments precipitate profound physical disability, substantially compromising patients' quality of life while imposing substantial socioeconomic burdens on families and healthcare systems [ 5 ]. Despite extensive research on the pathogenesis and treatment of PD, the treatment of PD still remains great challenges,due to its complex pathological mechanisms and difficulties in diagnosis during early stage[ 6 ]. Iron is an essential micronutrient in the brain that plays a pivotal in normal brain development and function[ 7 ].Within the central nervous system(CNS), iron participats in multiple fundamental physiological processes such as oxygen transport, DNA synthesis, neurotransmitter production, and mitochondrial respiration, etc[ 8 ][ 9 ].However,dysregulated iron metabolism and subsequent iron accumulation in cerebral have been implicated in the pathogenesis of some neurodegenerative disorders,such as Alzheimer’s disease (AD), Parkinson’s disease (PD) ,Huntington’s disease(HD) and amyotrophic lateral sclerosis(ALS)[ 10 ][ 11 ]. Neuroimaging studies and pathological postmortem analyses consistently demonstrate deposition of iron in PD brains, particularly within the substantia nigra[ 12 ][ 13 ]. Iron dyshomeostasis contributes to PD progression through multiple mechanisms:1)inducing of ferroptosis in dopaminergic neurons inPD, 2)promotion of α-synuclein (a-syn) pathological aggregation and conformational changes, 3)Fenton reaction-mediated generation of reactive oxygen species (ROS) and H2O2 by Fe 2+ ,leading to oxidative stress[ 14 ].The resulting oxidative damage accelerates neuroinflammation and neuronal death, creating a vicious cycle that Promotes the progression of disease[ 15 ].Therefore,modulation of iron metabolism and iron chelation therapies have emerged as promising therapeutic strategies for PD[ 16 ]. Three serum biomarkers are used to assess iron homeostasis:serum iron,ferritin, transferrin receptor (TFR).In recent studies,serum TfR and ferritin are wildly used to reflect iron status[17[ [ 18 ]. ferritin's utility is limited by susceptibility to inflammatory confounders and other factors [ 19 ]. In contrast,TfR directly reflects cellular iron demand,and is minimally influenced by systemic inflammation[ 20 ][ 21 ]. Emerging evidence highlights the clinical relevance of these biomarkers beyond traditional iron-deficiency anemia. Elevated serum ferritin levels correlate with cognitive impairment in a large sample survey[ 22 ].While iron related markers(Ferritin, transferrin, and transferrin receptor) associate with metabolic obesity[ 23 ].Another cross-sectional study found that soluble transferrin receptor(sTFR) independently predicts systolic hypertension [ 24 ].Despite these advances,large-scale investigations elucidating iron biomarkers in PD remain scarce.Therefore, we analyzed cross-sectional data from the National Health and Nutrition Examination Survey (NHANES)(2005–2010,2015–2018) to investigate the relationship between three iron biomarkers (serum iron,ferritin, TFR) and PD, Compare the predictive capacity of these biomarkers for PD risk. Materials and methods 2.1 Study Population and Data Source This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) (2005–2010,2015–2018),a nationally representative cross-sectional survey conducted by the National Center for Health Statistics (NCHS)to assess the health and nutritional status of the US population.The NHANES study protocol was approved by the NCHS Research Ethics Review Board,with all participants provided written informed consent.we initially identified 50,259 adults,After applying exclusion criteria,the final analytical sample consisted of 4,496 eligible participants.The exclusion criteria were as follows:1)Age < 40 years (n = 31,395);2)missing date on PD (n = 826) ;3)Without date on serum iron(n = 1831) ;Without data on ferritin (n = 10), Without data on transferrin receptor (n = 11701).The detailed participant selection process is illustrated in Fig. 1 .The dataset for this study are publicly available at: https://wwwn.cdc.gov/nchs/nhanes/NhanesCitation.aspx . 2.2 Detection of Iron-Related Biomarkers Serum iron levels were detected using the timed endpoint method on the following analyzer:Beckman Synchron LX20(2005–2010) ,Beckman DCX-800 system(2015–2016) ,Roche Cobas 6000 (2017–2018) [ 25 ]. The ferritin concentration were detected using the immunoturbidimetry method on the following analyzer:Hitachi 912 clinical analyzer(2005–2010) and the Roche Elecsys 170 clinical analyzer(2015–2018)[ 26 ]. Serum TFR concentration were detected using the particle-enhanced immunoturbidimetry by three chemical analyzers: Hitachi 912 ( 2005–2008), Hitachi Mod P (2009–2010 and 2015), and the Roche c501 analyzers (2016–2018) [ 27 ].The detailed laboratory protocols are available on the NHANES website. 2.3 Definition of Parkinson’s disease Consistent with previous NHANES-based studies on PD [ 28 ][ 29 ],PD cases were defined through self-reported use anti-Parkinson's drugs in health questionnaires. These drugs include Levodopa, Carbidopa,Entacapone,Amantadine, and so on.Those who have not taken such drugs were classified non-PD controls[ 30 ]. 2.4 Assessment of covariates Covariates included:Demographics:Age,gender,race(Mexican American,non-Hispanic black,Non-Hispanic white,other Hispanic,Other race),education(high school),marital(living alone/with a partner).Socioeconomic status: Poverty-income ratio(PIR,<1.30,1.30 ~ 3.49,≥3.50). Clinical measures: BMI (normal,obese,overweight), aspartate transaminase(AST),γ-Glutamyl transferase(GGT),total bilirubin(TB),uric acid(UA),high-density lipoprotein(HDL),triglycerides(TG);total cholesterol(TC),Low density lipoproteins(LDL).Comorbidities: hypertension (no, yes), dyslipidemia (no, yes),diabetes (no, yes).Lifestyle factors: smoke (no, yes), drink (no, yes),physical activity (PA; yes/no). 2.5 Statistical analysis Continuous variables: Expressed as mean ± standard deviation.categorical variables:Expressed as percentage. Multivariate logistic regression analyzed the association between TFR and PD in three models.Model 1(Unadjusted);Model 2:(Adjusted for age, gender, race, marital and education);Model 3(Adjusted for Model 2 combined with PIR,BMI,ALT, AST, HDL,TC,TG,LDL,hypertension, dyslipidemia, diabetes, smoke and drink,PA).Reference group:The first quartile group.Effect estimates:Odds ratios (ORs) and 95% confidence intervals (CIs).The smooth curve fitting analysis was used to determine the dose-response association between TFR and PD,subgroup analyses: Stratified by covariates to test interactions.In addition, we also conducted sensitivity analysis To verify robustness.Statistical significance was set at p < 0.05 (two-tailed). Results 3.1. Baseline characters of participants This study included 4,496 participants, including 74 PD cases and 4,422 non-PD controls.The average Age,Overrall:54.96 ± 12.46 years old,Non-PD group: 54.87 ± 12.42 years old, PD group(P < 0.05)): 60.38 ± 14.05 years old.Compared to Non-PD group, PD group had significantly higher in level of TfR ( p < 0.05). However, the two groups had no significant difference in serum rion level( p = 0.449) and Ferritin level ( p = 0.954). Additionally,The significant disparities were observed between PD and non-PD groups in race, hypertension and diabetes(p < 0.001). There were no significant differences in serum iron, ferritin, and TFR quartiles between the PD and Non-PD groups. Table 1 Characteristics of study participants from NHANES Variable Overall Non-PD PD p-value N = 4,496 N = 4,422 N = 74 Age, mean (sd) 54.96 (12.46) 54.87 (12.42) 60.38 (14.05) 0.001 PIR, mean (sd) 2.64 (1.54) 2.64 (1.54) 2.45 (1.47) 0.252 BMI, mean (sd) 29.99 (7.19) 29.95 (7.17) 32.41 (7.89) 0.009 ALT, mean (sd) 21.44 (13.66) 21.48 (13.72) 19.08 (9.02) 0.028 Albumin, mean (sd) 4.06 (0.32) 4.06 (0.32) 3.94 (0.33) 0.003 AST, mean (sd) 22.18 (10.85) 22.20 (10.90) 21.42 (6.81) 0.340 GGT, mean (sd) 29.49 (36.87) 29.38 (36.77) 35.73 (42.49) 0.206 TB, mean (sd) 0.50 (0.26) 0.50 (0.26) 0.46 (0.22) 0.061 UA, mean (sd) 5.18 (1.45) 5.18 (1.45) 5.53 (1.32) 0.027 HDL, mean (sd) 55.04 (15.85) 55.06 (15.85) 54.03 (15.87) 0.580 TC, mean (sd) 194.35 (40.55) 194.45 (40.44) 188.41 (46.74) 0.273 TG, mean (sd) 116.99 (60.93) 116.94 (61.09) 119.95 (50.06) 0.611 LDL, mean (sd) 113.75 (24.91) 113.82 (24.93) 109.76 (23.92) 0.152 iron, mean (sd) 81.98 (29.67) 82.02 (29.68) 79.45 (28.85) 0.449 TfR, mean (sd) 3.18 (0.92) 3.17 (0.92) 3.52 (1.02) 0.005 Ferritin, mean (sd) 107.12 (82.29) 107.13 (82.36) 106.59 (78.80) 0.954 Gender, n (p%) 0.287 Male 1,328.00 (29.54%) 1,302.00 (29.44%) 26.00 (35.14%) Female 3,168.00 (70.46%) 3,120.00 (70.56%) 48.00 (64.86%) Race, n (p%) < 0.001 Mexican American 684.00 (15.21%) 681.00 (15.40%) 3.00 (4.05%) Non-Hispanic Black 451.00 (10.03%) 447.00 (10.11%) 4.00 (5.41%) Non-Hispanic White 1,777.00 (39.52%) 1,724.00 (38.99%) 53.00 (71.62%) Other Hispanic 935.00 (20.80%) 925.00 (20.92%) 10.00 (13.51%) Other race 649.00 (14.44%) 645.00 (14.59%) 4.00 (5.41%) Education, n (p%) 0.500 Above high school 543.00 (12.08%) 531.00 (12.01%) 12.00 (16.22%) High school 984.00 (21.89%) 970.00 (21.94%) 14.00 (18.92%) Under high school 2,969.00 (66.04%) 2,921.00 (66.06%) 48.00 (64.86%) Marital, n (p%) 0.221 Living alone 2,562.00 (56.98%) 2,525.00 (57.10%) 37.00 (50.00%) Living with a partner 1,934.00 (43.02%) 1,897.00 (42.90%) 37.00 (50.00%) PIR_Group, n (p%) 0.584 < 1.30 752.00 (16.73%) 738.00 (16.69%) 14.00 (18.92%) 1.30 ~ 3.49 2,606.00 (57.96%) 2,561.00 (57.91%) 45.00 (60.81%) ≥ 3.50 1,138.00 (25.31%) 1,123.00 (25.40%) 15.00 (20.27%) BMI_Group, n (p%) 0.009 Normal 1,121.00 (24.93%) 1,107.00 (25.03%) 14.00 (18.92%) Obese 1,421.00 (31.61%) 1,406.00 (31.80%) 15.00 (20.27%) Overweight 1,954.00 (43.46%) 1,909.00 (43.17%) 45.00 (60.81%) Hypertension, n (p%) < 0.001 No 2,707.00 (60.21%) 2,682.00 (60.65%) 25.00 (33.78%) Yes 1,789.00 (39.79%) 1,740.00 (39.35%) 49.00 (66.22%) Dyslipidemia, n (p%) 0.012 No 2,644.00 (58.81%) 2,611.00 (59.05%) 33.00 (44.59%) Yes 1,852.00 (41.19%) 1,811.00 (40.95%) 41.00 (55.41%) Diabetes, n (p%) < 0.001 No 3,744.00 (83.27%) 3,695.00 (83.56%) 49.00 (66.22%) Yes 752.00 (16.73%) 727.00 (16.44%) 25.00 (33.78%) Smoke, n (p%) 0.005 No 2,602.00 (57.87%) 2,571.00 (58.14%) 31.00 (41.89%) Yes 1,894.00 (42.13%) 1,851.00 (41.86%) 43.00 (58.11%) Drink, n (p%) 0.319 No 1,709.00 (38.01%) 1,685.00 (38.10%) 24.00 (32.43%) Yes 2,787.00 (61.99%) 2,737.00 (61.90%) 50.00 (67.57%) PA, n (p%) 0.034 No 2,746.00 (61.08%) 2,692.00 (60.88%) 54.00 (72.97%) Yes 1,750.00 (38.92%) 1,730.00 (39.12%) 20.00 (27.03%) Fe_EqualSizeBin, n (p%) 0.217 Q1 1,124.00 (25.00%) 1,107.00 (25.03%) 17.00 (22.97%) Q2 1,124.00 (25.00%) 1,100.00 (24.88%) 24.00 (32.43%) Q3 1,124.00 (25.00%) 1,103.00 (24.94%) 21.00 (28.38%) Q4 1,124.00 (25.00%) 1,112.00 (25.15%) 12.00 (16.22%) TFR_EqualSizeBin, n (p%) 0.069 Q1 1,124.00 (25.00%) 1,111.00 (25.12%) 13.00 (17.57%) Q2 1,124.00 (25.00%) 1,111.00 (25.12%) 13.00 (17.57%) Q3 1,124.00 (25.00%) 1,102.00 (24.92%) 22.00 (29.73%) Q4 1,124.00 (25.00%) 1,098.00 (24.83%) 26.00 (35.14%) Ferritin_EqualSizeBin, n (p%) 0.136 Q1 1,124.00 (25.00%) 1,111.00 (25.12%) 13.00 (17.57%) Q2 1,124.00 (25.00%) 1,098.00 (24.83%) 26.00 (35.14%) Q3 1,124.00 (25.00%) 1,104.00 (24.97%) 20.00 (27.03%) Q4 1,124.00 (25.00%) 1,109.00 (25.08%) 15.00 (20.27%) NHANES,National Health and Nutrition Examination Survey.PIR,Poverty-income ratio;BMI,body mass index;ALT,alanine aminotransferase;AST,aspartate transaminase;GGT,γ-Glutamyl transferase;TB,total bilirubin;UA,Uric Acid;HDL,high-density lipoprotein;TG,triglycerides;TC,total cholesterol;LDL,Low density lipoproteins;TfR,transferrin receptor;PA,physical activity,Q1,quartile1;Q2,quartile2;Q3,quartile3;Q4,quartile4;OR,odds ratio;95%CI,95% confidence interval. 3.2 Association between TfR and PD The logistic regression analysis demonstrates a consistent positive association between TfR levels and PD risk across progressively adjusted models (Table 2 ), In Model 1(Unadjusted), compared to the lowest quartile group, the highest quartile group had the higher risk of developing PD (OR 2.8963, 95% CI 2.5448–3.2989, p < 0.0001).In Model 2( Adjusted for age, gender, race, marital and education),compared to the lowest quartile group,the highest quartile group,had the higher risk of developing PD (OR 2.5822, 95% CI 2.2151–3.0126, p < 0.0001).In Model 3(Further adjusted for PIR,BMI,ALT,AST,HDL,TC,TG,LDL,hypertension, dyslipidemia,diabetes,smoke and drink,PA),compared to the lowest quartile group,the highest quartile group,had the higher risk of developing PD(OR 2.5141, 95% CI 2.0834–3.0367, p < 0.0001).Serum TfR levels exhibit a significant positive correlation with PD risk (p < 0.0001), with higher TfR quartiles consistently associated with increased PD incidence despite comprehensive adjustment for demographic, metabolic, and lifestyle confounders. This supports TfR's potential role in PD pathogenesis through iron homeostasis dysregulation. Table 2 Associations between TfR and PD from the NHANES. Variable OR (95% CI), p-value Model 1 Model 2 Model 3 TfR 1.4022(1.1343,1.7224), 0.0015 1.4631(1.1741,1.8113), 0.0006 1.3041(1.0381,1.6278), 0.0204 Stratified by TfR tertiles Q1 Reference Reference Reference Q2 2.0585(1.8119,2.3398), < 0.0001 1.9522(1.6835,2.2650), < 0.0001 1.9406(1.6244,2.3199), < 0.0001 Q3 3.1508(2.7662,3.5918), < 0.0001 2.8204(2.4233,3.2855), < 0.0001 2.7070(2.2525,3.2564), < 0.0001 Q4 2.8963(2.5448,3.2989), < 0.0001 2.5822(2.2151,3.0126), < 0.0001 2.5141(2.0834,3.0367), < 0.0001 P for trend < 0.0001 < 0.0001 < 0.0001 Model 1: Adjusted for none. Model 2: Adjusted for age, gender, race, marital and education. Model 3:Adjusted for Model 2 combined withPIR,BMI,ALT,AST,HDL,TC,TG,LDL,hypertension, dyslipidemia,diabetes,smoke and drink,PA. 3.3 Nonlinear Relationship between the level of TfR and PD We applied smooth curve fitting to explore the nonlinear relationship between TfR and PD. This is a non-parametric technique that visualizes exposure-outcome relationships without presupposing functional forms .This analysis confirmed a significant nonlinear positive correlation between elevated TfR levels and PD risk (Fig. 2 ). 3.4 Subgroup analysis To assess the robustness of the TFR levels and PD association, we performed pre-specified subgroup analyses and stratified by key covariates: BMI, hypertension, dyslipidemia,diabetes,smoke, drink, PA(Fig. 3 ).interaction tests confirmed no significant effect modification across any subgroup (all p-interaction > 0.05).The research results indicates that the TfR-PD relationship persists across heterogeneous populations independent of these metabolic and lifestyle confounders. This consistency demonstrates TfR as a stable biomarker for PD risk stratification in diverse demographic and clinical cohorts. 3.5 Sensitivity analysis We implemented sensitivity analysis by categorizing serum TfR levels into three quartiles (T1: lowest, T2, T3: highest) and conducting stratified logistic regression to validate the robustness of the primary findings.In Model 1 (Unadjusted), Participants in the highest TfR T3 exhibited significantly higher PD risk compared to T1 (OR 2.4872, 95% CI 2.2251–2.7815, p < 0.0001).In Model 2 ( Adjusted for demographic: age, gender, race, marital and education.),Participants in the highest TfR T3 exhibited significantly higher PD risk compared to T1 (OR 2.2238, 95% CI 1.9468–2.5416, p < 0.0001).In Model 3(Further adjusted for PIR, BMI, liver enzymes (ALT/AST), lipid profiles (HDL, TC, TG, LDL), comorbidities (hypertension, dyslipidemia, diabetes), and lifestyle factors (smoking, alcohol, physical activity)),The Q3 group maintained elevated PD risk versus Q1(OR 2.1340, 95% CI 1.8116–2.5155, p < 0.0001).Both continuous and categorical TfR variables demonstrated a positive correlation with PD incidence in sensitivity analyses (all p < 0.001). Table 3 Logistic regression analysis on the relationship between TFR and PD in the Sensitivity analysis Variable OR (95% CI), p-value Model 1 Model 2 Model 3 TFR 1.4022(1.1343,1.7224), 0.0015 1.4631(1.1741,1.8113), 0.0006 1.3041(1.0381,1.6278), 0.0204 Stratified by TfR tertiles T1 Reference Reference Reference T2 2.0172(1.8070,2.2527), < 0.0001 2.0343(1.7876,2.3162), < 0.0001 2.0199(1.7273,2.3637), < 0.0001 T3 2.4872(2.2251,2.7815), < 0.0001 2.2238(1.9468,2.5416), < 0.0001 2.1340(1.8116,2.5155), < 0.0001 P for trend < 0.0001 < 0.0001 < 0.0001 Model 1: Adjusted for none. Model 2: Adjusted for age, gender, race, marital and education. Model 3:Adjusted for Model 2 combined withPIR,BMI,ALT,AST,HDL,TC,TG,LDL,hypertension, dyslipidemia,diabetes,smoke and drink,PA.T1,Tertile 1;T2,Tertile 2;T3,Tertile 3. Discussion Our population-based study investigated the association between peripheral iron metabolism biomarkers and PD risk in a American adults aged ≥ 40 years (NHANES 2005–2010 and 2015–2018). The key findings indicated a significant positive association between serum TfR levels and PD risk (p < 0.05), while no such associations were observed between serum iron or ferritin concentrations and PD. The finding suggests that serum TfR levels may serve as a promising peripheral biomarker for PD risk stratification. The consistency of this association across multiple analytical approaches, coupled with the observed dose-response relationship, supports the potential clinical utility of TfR measurement in PD risk assessment. As is well known,iron homeostasis plays a crucial role in maintaining normal neurological function[ 31 ][ 32 ].As an essential cofactor of ribonucleotide reductase, iron is indispensable for DNA synthesis, cellular proliferation, and proper neural tube development [ 33 ].In the central nervous system, iron participates in critical neurodevelopmental processes including synaptogenesis and neurotransmitter synthesis [ 32 , 34 ].Studies have demonstrated that iron deficiency may contribute to various cardiovascular pathologies, including coronary artery disease and pulmonary hypertension [ 35 ].However,the excessive iron can trigger oxidative damage through Fenton chemistry, leading to lipid peroxidation, mitochondrial impairment, and ultimately ferroptosis [ 36 ].Currently, many studies have also confirmed that iron overload induces ferroptosis of dopamine neurons in PD[ 37 ][ 38 ]. At present, Current research has firmly established the pathological accumulation of iron in the substantia nigra pars compacta (SNpc) of PD patients, detection of iron metabolism markers are expected to become promising biomarkers for early diagnosis and disease progression monitoring [ 39 – 41 ]. However, investigations in some studies into peripheral and cerebrospinal fluid (CSF) iron markers have inconsistent results.A case-control study demonstrated altered CSF iron levels in PD patients while peripheral iron levels remained unchanged [ 42 ]. A meta-analytic data showed no significant differences in serum/CSF iron markers between PD patients and healthy controls [ 43 ].Contrasting evidence suggests an inverse relationship between serum and CSF iron levels in PD pathogenesis[ 44 ][ 45 ]. The heterogeneity observed across these studies may stem from systemic iron metabolism dysregulation in PD patients[ 46 ]. Additionally, disruption of iron homeostasis leads to aberrant iron redistribution, driving pathological accumulation in the SNpc region, which consequently reduces peripheral blood levels of iron-related biomarkers[ 47 ].In our investigation, serum iron and ferritin levels exhibited no statistically significant differences between the PD group and the non-PD group. These biomarkers are susceptible to change by confounding variables, particularly inflammation, therefore diminishing their diagnostic reliability for assessing systemic iron homeostasis. TfR is a cleaved monomer of TfR1, represents a novel and robust biomarker of iron status. Unlike other iron markers, sTfR demonstrates superior stability as it remains unaffected by inflammatory processes, providing a more reliable reflection of systemic iron metabolism [ 48 ]. Emerging evidence demonstrate the clinical significance of sTfR in various disease,the elevated sTfR concentrations correlate with impaired iron utilization and increased cardiovascular disease risk[ 49 ].In obese populations, higher sTfR levels predict greater susceptibility to type 2 diabetes [ 50 ].TfR serves as an independent prognostic indicator for patients with diabetes-related coronary heart disease or heart failure [ 51 , 52 ].Recent investigations identified exosomal ferritin and TfR as potential PD biomarkers, suggesting their involvement in disease pathogenesis [ 53 ].Our finding revealed Significant positive association between TfR levels and PD risk.The smoothing curve analysis plot indicates that as the level of TFR gradually increases, the risk of PD gradually increases.The TfR may be involved in the pathogenesis of PD.Dysregulation of iron homeostasis is often accompanied by chronic inflammation. Studies have found that TfR is positively correlated with CRP [ 54 ] and antioxidant status. The elevated TfR level in multiple sclerosis patients may indicates active inflammation accompanied by persistent oxidative damage [ 55 ]. Iron homeostasis dysregulation and inflammatory response also exist in PD patients, which may be the reason for elevated TFR, and deserves further exploration. Strength:This study is the first to explore the association between iron metabolism biomarker and PD risk based on NHANES data. The sample size is large and includes multiple races, which improves the accuracy of the study; In addition, this study compared the correlation between three iron metabolism biomarker and PD risk ,and found a positive correlation between TFR concentration and PD risk. Smoothed curve fitting analysis was employed to evaluate the dose-response relationship between TFR and PD, subgroup analysis and sensitivity analysis were performed to demonstrate the correlation’s robustness.Limitations,The reliance on self-reported anti-parkinsonian medication use as a surrogate for PD diagnosis may introduce selection bias.potentially excluding: 1)PD patients with mild symptoms who did not receive medication intervention, 2)Undiagnosed individuals in early disease stages. To overcome these issues, future research requires comprehensive evaluation by certified specialists, In addition, MDS-UPDRS Part III motor examination and Dopamine transporter (DAT) imaging can be combined to improve the accuracy of diagnosis.While this cross-sectional investigation provides preliminary evidence regarding iron dysregulation in PD, the unidimensional assessment fails to capture the temporal dynamics of iron homeostasis markers.Therefore, longitudinal follow-up and repeated measurements of these indicators will make the results more reliable. Conclusion TFR levels demonstrate a significant positive correlation with PD risk, positioning TFR as a potential biomarker for early PD prediction. Higher serum TFR concentrations may indicate elevated susceptibility to PD onset, while interventions reducing TFR levels could mitigate disease risk. Declarations Authors' Contribution Xianwen Chen proposed the topic; Yan Su analyzed the structure and intellectual content of this article and made critical revisions. Sheng Cai ,Yang Xu prepared revised the manuscript. All the authors agreed to publish this article. Availability of Data and Materials The data that support the findings of this study are available from the author upon reasonable request. Funding The present study was supported by the National Natural Science Foundation of China[grant number 81971072]. Ethics approval and Consent to Participate This study only used publicly available data and no additional informed consent is required. Competing Interests The authors declare no competing interests. Consent for Publication Not applicable. References Tolosa E, Garrido A, Scholz SW, Poewe W.Challenges in the diagnosis of Parkinson’s disease,Lancet Neurol. 2021, May;20(5):385-397. doi: 10.1016/S1474-4422(21)00030-2. Bloem BR, Okun MS, Klein C. Parkinson’s disease. 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Iron status and survival in diabetic patients with coronary artery disease. Diabetes Care . 2013;36:4147–4156. doi: 10.2337/dc13-0528. Chen ZT, Pan CZ, Ruan XL, Lei LP, Lin SM, Wang YZ, et al,Evaluation of ferritin and TfR level in plasma neural-derived exosomes as potential markers of Parkinson’s disease,Front Aging Neurosci. 2023, Sep 19;15:1216905. doi: 10.3389/fnagi.2023.1216905. Zawadzki B, Mazur G, Butrym A. Iron dysregulation and frailty syndrome. J Clin Med. 2021,10:5596. doi: 10.3390/jcm10235596. Sfagos C, Makis AC, Chaidos A, Hatzimichael EC, Dalamaga A, Kosma K, et al. Serum ferritin, transferrin and soluble transferrin receptor levels in multiple sclerosis patients. Mult Scler. (2005) 11:272–5. doi: 10.1191/1352458505ms1171oa. Additional Declarations No competing interests reported. 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15:45:59","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":189899,"visible":true,"origin":"","legend":"","description":"","filename":"adcf23291a304f8ea5200ee9d2f5d5051structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7672096/v1/81a9ec33100401f4fb9de2b9.xml"},{"id":93251058,"identity":"07409062-523c-4246-97e9-2d4e31b4078c","added_by":"auto","created_at":"2025-10-10 15:45:59","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195754,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7672096/v1/d28c3ff56ccbb39743e3ec8f.html"},{"id":93251037,"identity":"102cdeca-6b73-4999-9831-9c3b67440f91","added_by":"auto","created_at":"2025-10-10 15:45:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279062,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participants screen\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7672096/v1/266c9f42a43a6ae3cf2bb485.png"},{"id":93251040,"identity":"e4bb740e-8198-43f5-8812-20c1ad62ada1","added_by":"auto","created_at":"2025-10-10 15:45:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":518334,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear association between TFR and PD.The solid blue line represents the smooth curve fit between two variables.The gray dashed line represents the 95% confidence interval of the fit.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7672096/v1/978e2377002db12b5e407bcd.png"},{"id":93252746,"identity":"7b4ffb5e-8c01-476b-b0a5-75be73478c9e","added_by":"auto","created_at":"2025-10-10 16:01:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1370403,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate subgroup GLM regression overall forest plot.Subgroup analysis stratified by BMI,hypertension,dyslipidemia,diabetes,smoke, drink, PA. OR,odds ratio;95%CI,95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7672096/v1/1dae148c6ba54176263ba280.png"},{"id":95655514,"identity":"c63bdf04-dc32-451a-a3d0-f1dfaf590a45","added_by":"auto","created_at":"2025-11-11 16:16:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2913739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7672096/v1/5679d7e6-3712-412c-a4cd-9bd23dfbbb42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between serum transferrin receptor and Parkinson's disease: A large cross-sectional study from NHANES","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson's disease (PD),ranking as the second most prevalent neurodegenerative disorder globally, currently impacts over 6\u0026nbsp;million individuals globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Epidemiological projections indicate a doubling of PD patients to exceed 12\u0026nbsp;million by 2040, primarily caused by population aging trends[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Clinically, PD manifests through a spectrum of motor dysfunction (e.g., resting tremor, bradykinesia) and non-motor features including sleep fragmentation and cognitive decline\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e],These multisystem impairments precipitate profound physical disability, substantially compromising patients' quality of life while imposing substantial socioeconomic burdens on families and healthcare systems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite extensive research on the pathogenesis and treatment of PD, the treatment of PD still remains great challenges,due to its complex pathological mechanisms and difficulties in diagnosis during early stage[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIron is an essential micronutrient in the brain that plays a pivotal in normal brain development and function[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Within the central nervous system(CNS), iron participats in multiple fundamental physiological processes such as oxygen transport, DNA synthesis, neurotransmitter production, and mitochondrial respiration, etc[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].However,dysregulated iron metabolism and subsequent iron accumulation in cerebral have been implicated in the pathogenesis of some neurodegenerative disorders,such as Alzheimer\u0026rsquo;s disease (AD), Parkinson\u0026rsquo;s disease (PD) ,Huntington\u0026rsquo;s disease(HD) and amyotrophic lateral sclerosis(ALS)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNeuroimaging studies and pathological postmortem analyses consistently demonstrate deposition of iron in PD brains, particularly within the substantia nigra[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Iron dyshomeostasis contributes to PD progression through multiple mechanisms:1)inducing of ferroptosis in dopaminergic neurons inPD, 2)promotion of α-synuclein (a-syn) pathological aggregation and conformational changes, 3)Fenton reaction-mediated generation of reactive oxygen species (ROS) and H2O2 by Fe\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e,leading to oxidative stress[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].The resulting oxidative damage accelerates neuroinflammation and neuronal death, creating a vicious cycle that Promotes the progression of disease[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].Therefore,modulation of iron metabolism and iron chelation therapies have emerged as promising therapeutic strategies for PD[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThree serum biomarkers are used to assess iron homeostasis:serum iron,ferritin, transferrin receptor (TFR).In recent studies,serum TfR and ferritin are wildly used to reflect iron status[17[ [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. ferritin's utility is limited by susceptibility to inflammatory confounders and other factors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast,TfR directly reflects cellular iron demand,and is minimally influenced by systemic inflammation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmerging evidence highlights the clinical relevance of these biomarkers beyond traditional iron-deficiency anemia. Elevated serum ferritin levels correlate with cognitive impairment in a large sample survey[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].While iron related markers(Ferritin, transferrin, and transferrin receptor) associate with metabolic obesity[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Another cross-sectional study found that soluble transferrin receptor(sTFR) independently predicts systolic hypertension [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].Despite these advances,large-scale investigations elucidating iron biomarkers in PD remain scarce.Therefore, we analyzed cross-sectional data from the National Health and Nutrition Examination Survey (NHANES)(2005\u0026ndash;2010,2015\u0026ndash;2018) to investigate the relationship between three iron biomarkers (serum iron,ferritin, TFR) and PD, Compare the predictive capacity of these biomarkers for PD risk.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population and Data Source\u003c/h2\u003e\u003cp\u003eThis study analyzed data from the National Health and Nutrition Examination Survey (NHANES) (2005\u0026ndash;2010,2015\u0026ndash;2018),a nationally representative cross-sectional survey conducted by the National Center for Health Statistics (NCHS)to assess the health and nutritional status of the US population.The NHANES study protocol was approved by the NCHS Research Ethics Review Board,with all participants provided written informed consent.we initially identified 50,259 adults,After applying exclusion criteria,the final analytical sample consisted of 4,496 eligible participants.The exclusion criteria were as follows:1)Age\u0026thinsp;\u0026lt;\u0026thinsp;40 years (n\u0026thinsp;=\u0026thinsp;31,395);2)missing date on PD (n\u0026thinsp;=\u0026thinsp;826) ;3)Without date on serum iron(n\u0026thinsp;=\u0026thinsp;1831) ;Without data on ferritin (n\u0026thinsp;=\u0026thinsp;10), Without data on transferrin receptor (n\u0026thinsp;=\u0026thinsp;11701).The detailed participant selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e .The dataset for this study are publicly available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/NhanesCitation.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/NhanesCitation.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Detection of Iron-Related Biomarkers\u003c/h2\u003e\u003cp\u003eSerum iron levels were detected using the timed endpoint method on the following analyzer:Beckman Synchron LX20(2005\u0026ndash;2010) ,Beckman DCX-800 system(2015\u0026ndash;2016) ,Roche Cobas 6000 (2017\u0026ndash;2018) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe ferritin concentration were detected using the immunoturbidimetry method on the following analyzer:Hitachi 912 clinical analyzer(2005\u0026ndash;2010) and the Roche Elecsys 170 clinical analyzer(2015\u0026ndash;2018)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSerum TFR concentration were detected using the particle-enhanced immunoturbidimetry by three chemical analyzers: Hitachi 912 ( 2005\u0026ndash;2008), Hitachi Mod P (2009\u0026ndash;2010 and 2015), and the Roche c501 analyzers (2016\u0026ndash;2018) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].The detailed laboratory protocols are available on the NHANES website.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Definition of Parkinson\u0026rsquo;s disease\u003c/h2\u003e\u003cp\u003eConsistent with previous NHANES-based studies on PD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e],PD cases were defined through self-reported use anti-Parkinson's drugs in health questionnaires. These drugs include Levodopa, Carbidopa,Entacapone,Amantadine, and so on.Those who have not taken such drugs were classified non-PD controls[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Assessment of covariates\u003c/h2\u003e\u003cp\u003eCovariates included:Demographics:Age,gender,race(Mexican American,non-Hispanic black,Non-Hispanic white,other Hispanic,Other race),education(\u0026lt;\u0026thinsp;high school,high school,\u0026gt;high school),marital(living alone/with a partner).Socioeconomic status: Poverty-income ratio(PIR,\u0026lt;1.30,1.30\u0026thinsp;~\u0026thinsp;3.49,\u0026ge;3.50). Clinical measures: BMI (normal,obese,overweight), aspartate transaminase(AST),γ-Glutamyl transferase(GGT),total bilirubin(TB),uric acid(UA),high-density lipoprotein(HDL),triglycerides(TG);total cholesterol(TC),Low density lipoproteins(LDL).Comorbidities: hypertension (no, yes), dyslipidemia (no, yes),diabetes (no, yes).Lifestyle factors: smoke (no, yes), drink (no, yes),physical activity (PA; yes/no).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables: Expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.categorical variables:Expressed as percentage. Multivariate logistic regression analyzed the association between TFR and PD in three models.Model 1(Unadjusted);Model 2:(Adjusted for age, gender, race, marital and education);Model 3(Adjusted for Model 2 combined with PIR,BMI,ALT, AST, HDL,TC,TG,LDL,hypertension, dyslipidemia, diabetes, smoke and drink,PA).Reference group:The first quartile group.Effect estimates:Odds ratios (ORs) and 95% confidence intervals (CIs).The smooth curve fitting analysis was used to determine the dose-response association between TFR and PD,subgroup analyses: Stratified by covariates to test interactions.In addition, we also conducted sensitivity analysis To verify robustness.Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Baseline characters of participants\u003c/h2\u003e\u003cp\u003eThis study included 4,496 participants, including 74 PD cases and 4,422 non-PD controls.The average Age,Overrall:54.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46 years old,Non-PD group: 54.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42 years old, PD group(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)): 60.38\u0026thinsp;\u0026plusmn;\u0026thinsp;14.05 years old.Compared to Non-PD group, PD group had significantly higher in level of TfR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the two groups had no significant difference in serum rion level(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.449) and Ferritin level (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.954). Additionally,The significant disparities were observed between PD and non-PD groups in race, hypertension and diabetes(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences in serum iron, ferritin, and TFR quartiles between the PD and Non-PD groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants from NHANES\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-PD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.96 (12.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.87 (12.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.38 (14.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.64 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.64 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.45 (1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.99 (7.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.95 (7.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.41 (7.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.44 (13.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.48 (13.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.08 (9.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.06 (0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.06 (0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.94 (0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.18 (10.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.20 (10.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.42 (6.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.340\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.49 (36.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.38 (36.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.73 (42.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46 (0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.18 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.18 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.53 (1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.04 (15.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.06 (15.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.03 (15.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194.35 (40.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194.45 (40.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e188.41 (46.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116.99 (60.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116.94 (61.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.95 (50.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.75 (24.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.82 (24.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109.76 (23.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eiron, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.98 (29.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.02 (29.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.45 (28.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTfR, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.18 (0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.17 (0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.52 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFerritin, mean (sd)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.12 (82.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.13 (82.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.59 (78.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,328.00 (29.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,302.00 (29.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.00 (35.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,168.00 (70.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,120.00 (70.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.00 (64.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e684.00 (15.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e681.00 (15.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (4.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e451.00 (10.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e447.00 (10.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 (5.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,777.00 (39.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,724.00 (38.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.00 (71.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e935.00 (20.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e925.00 (20.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.00 (13.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e649.00 (14.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e645.00 (14.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 (5.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbove high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e543.00 (12.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e531.00 (12.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.00 (16.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e984.00 (21.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e970.00 (21.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.00 (18.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,969.00 (66.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,921.00 (66.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.00 (64.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,562.00 (56.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,525.00 (57.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.00 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with a partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,934.00 (43.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,897.00 (42.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.00 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIR_Group, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e752.00 (16.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e738.00 (16.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.00 (18.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.30\u0026thinsp;~\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,606.00 (57.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,561.00 (57.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.00 (60.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,138.00 (25.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,123.00 (25.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (20.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI_Group, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,121.00 (24.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,107.00 (25.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.00 (18.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,421.00 (31.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,406.00 (31.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (20.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,954.00 (43.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,909.00 (43.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.00 (60.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,707.00 (60.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,682.00 (60.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.00 (33.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,789.00 (39.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,740.00 (39.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.00 (66.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,644.00 (58.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,611.00 (59.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.00 (44.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,852.00 (41.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,811.00 (40.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.00 (55.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,744.00 (83.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,695.00 (83.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.00 (66.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e752.00 (16.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e727.00 (16.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.00 (33.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,602.00 (57.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,571.00 (58.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.00 (41.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,894.00 (42.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,851.00 (41.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.00 (58.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,709.00 (38.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,685.00 (38.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.00 (32.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,787.00 (61.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,737.00 (61.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.00 (67.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePA, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,746.00 (61.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,692.00 (60.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.00 (72.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,750.00 (38.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,730.00 (39.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.00 (27.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFe_EqualSizeBin, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,107.00 (25.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.00 (22.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,100.00 (24.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.00 (32.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,103.00 (24.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.00 (28.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,112.00 (25.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.00 (16.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFR_EqualSizeBin, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,111.00 (25.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.00 (17.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,111.00 (25.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.00 (17.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,102.00 (24.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.00 (29.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,098.00 (24.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.00 (35.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFerritin_EqualSizeBin, n (p%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,111.00 (25.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.00 (17.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,098.00 (24.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.00 (35.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,104.00 (24.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.00 (27.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,124.00 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,109.00 (25.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (20.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNHANES,National Health and Nutrition Examination Survey.PIR,Poverty-income ratio;BMI,body mass index;ALT,alanine aminotransferase;AST,aspartate transaminase;GGT,γ-Glutamyl transferase;TB,total bilirubin;UA,Uric Acid;HDL,high-density lipoprotein;TG,triglycerides;TC,total cholesterol;LDL,Low density lipoproteins;TfR,transferrin receptor;PA,physical activity,Q1,quartile1;Q2,quartile2;Q3,quartile3;Q4,quartile4;OR,odds ratio;95%CI,95% confidence interval.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.2\u003c/b\u003e Association between TfR and PD\u003c/h2\u003e\u003cp\u003eThe logistic regression analysis demonstrates a consistent positive association between TfR levels and PD risk across progressively adjusted models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e),\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIn Model 1(Unadjusted), compared to the lowest quartile group, the highest quartile\u003c/h3\u003e\n\u003cp\u003egroup had the higher risk of developing PD (OR 2.8963, 95% CI 2.5448\u0026ndash;3.2989, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).In Model 2( Adjusted for age, gender, race, marital and education),compared to the lowest quartile group,the highest quartile group,had the higher risk of developing PD (OR 2.5822, 95% CI 2.2151\u0026ndash;3.0126, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).In Model 3(Further adjusted for PIR,BMI,ALT,AST,HDL,TC,TG,LDL,hypertension, dyslipidemia,diabetes,smoke and drink,PA),compared to the lowest quartile group,the highest quartile group,had the higher risk of developing PD(OR 2.5141, 95% CI 2.0834\u0026ndash;3.0367, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).Serum TfR levels exhibit a significant positive correlation with PD risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with higher TfR quartiles consistently associated with increased PD incidence despite comprehensive adjustment for demographic, metabolic, and lifestyle confounders. This supports TfR's potential role in PD pathogenesis through iron homeostasis dysregulation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between TfR and PD from the NHANES.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOR (95% CI), p-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTfR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4022(1.1343,1.7224),\u003c/p\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4631(1.1741,1.8113),\u003c/p\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3041(1.0381,1.6278),\u003c/p\u003e\u003cp\u003e0.0204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratified by TfR tertiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0585(1.8119,2.3398),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9522(1.6835,2.2650),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9406(1.6244,2.3199),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1508(2.7662,3.5918),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8204(2.4233,3.2855),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7070(2.2525,3.2564),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8963(2.5448,3.2989),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5822(2.2151,3.0126),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5141(2.0834,3.0367),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1: Adjusted for none.\u003c/p\u003e\u003cp\u003eModel 2: Adjusted for age, gender, race, marital and education.\u003c/p\u003e\u003cp\u003eModel 3:Adjusted for Model 2 combined withPIR,BMI,ALT,AST,HDL,TC,TG,LDL,hypertension, dyslipidemia,diabetes,smoke and drink,PA.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Nonlinear Relationship between the level of TfR and PD\u003c/h2\u003e\u003cp\u003eWe applied smooth curve fitting to explore the nonlinear relationship between TfR and PD. This is a non-parametric technique that visualizes exposure-outcome relationships without presupposing functional forms .This analysis confirmed a significant nonlinear positive correlation between elevated TfR levels and PD risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subgroup analysis\u003c/h2\u003e\u003cp\u003eTo assess the robustness of the TFR levels and PD association, we performed pre-specified subgroup analyses and stratified by key covariates: BMI, hypertension, dyslipidemia,diabetes,smoke, drink, PA(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).interaction tests confirmed no significant effect modification across any subgroup (all p-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).The research results indicates that the TfR-PD relationship persists across heterogeneous populations independent of these metabolic and lifestyle confounders.\u003c/p\u003e\u003cp\u003eThis consistency demonstrates TfR as a stable biomarker for PD risk stratification in diverse demographic and clinical cohorts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eWe implemented sensitivity analysis by categorizing serum TfR levels into three quartiles (T1: lowest, T2, T3: highest) and conducting stratified logistic regression to validate the robustness of the primary findings.In Model 1 (Unadjusted), Participants in the highest TfR T3 exhibited significantly higher PD risk compared to T1 (OR 2.4872, 95% CI 2.2251\u0026ndash;2.7815, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).In Model 2 ( Adjusted for demographic: age, gender, race, marital and education.),Participants in the highest TfR T3 exhibited significantly higher PD risk compared to T1 (OR 2.2238, 95% CI 1.9468\u0026ndash;2.5416, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).In Model 3(Further adjusted for PIR, BMI, liver enzymes (ALT/AST), lipid profiles (HDL, TC, TG, LDL), comorbidities (hypertension, dyslipidemia, diabetes), and lifestyle factors (smoking, alcohol, physical activity)),The Q3 group maintained elevated PD risk versus Q1(OR 2.1340, 95% CI 1.8116\u0026ndash;2.5155, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).Both continuous and categorical TfR variables demonstrated a positive correlation with PD incidence in sensitivity analyses (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis on the relationship between TFR and PD in the Sensitivity analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOR (95% CI), p-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4022(1.1343,1.7224),\u003c/p\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4631(1.1741,1.8113),\u003c/p\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3041(1.0381,1.6278),\u003c/p\u003e\u003cp\u003e0.0204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStratified by TfR tertiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0172(1.8070,2.2527),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0343(1.7876,2.3162),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0199(1.7273,2.3637),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4872(2.2251,2.7815),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2238(1.9468,2.5416),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1340(1.8116,2.5155),\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1: Adjusted for none.\u003c/p\u003e\u003cp\u003eModel 2: Adjusted for age, gender, race, marital and education.\u003c/p\u003e\u003cp\u003eModel 3:Adjusted for Model 2 combined withPIR,BMI,ALT,AST,HDL,TC,TG,LDL,hypertension, dyslipidemia,diabetes,smoke and drink,PA.T1,Tertile 1;T2,Tertile 2;T3,Tertile 3.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur population-based study investigated the association between peripheral iron metabolism biomarkers and PD risk in a American adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years (NHANES 2005\u0026ndash;2010 and 2015\u0026ndash;2018). The key findings indicated a significant positive association between serum TfR levels and PD risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no such associations were observed between serum iron or ferritin concentrations and PD.\u003c/p\u003e\u003cp\u003eThe finding suggests that serum TfR levels may serve as a promising peripheral biomarker for PD risk stratification. The consistency of this association across multiple analytical approaches, coupled with the observed dose-response relationship, supports the potential clinical utility of TfR measurement in PD risk assessment.\u003c/p\u003e\u003cp\u003eAs is well known,iron homeostasis plays a crucial role in maintaining normal neurological function[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e][\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].As an essential cofactor of ribonucleotide reductase, iron is indispensable for DNA synthesis, cellular proliferation, and proper neural tube development [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].In the central nervous system, iron participates in critical neurodevelopmental processes including synaptogenesis and neurotransmitter synthesis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].Studies have demonstrated that iron deficiency may contribute to various cardiovascular pathologies, including coronary artery disease and pulmonary hypertension [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].However,the excessive iron can trigger oxidative damage through Fenton chemistry, leading to lipid peroxidation, mitochondrial impairment, and ultimately ferroptosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].Currently, many studies have also confirmed that iron overload induces ferroptosis of dopamine neurons in PD[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e][\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt present, Current research has firmly established the pathological accumulation of iron in the substantia nigra pars compacta (SNpc) of PD patients, detection of iron metabolism markers are expected to become promising biomarkers for early diagnosis and disease progression monitoring [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, investigations in some studies into peripheral and cerebrospinal fluid (CSF) iron markers have inconsistent results.A case-control study demonstrated altered CSF iron levels in PD patients while peripheral iron levels remained unchanged [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A meta-analytic data showed no significant differences in serum/CSF iron markers between PD patients and healthy controls [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].Contrasting evidence suggests an inverse relationship between serum and CSF iron levels in PD pathogenesis[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e][\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe heterogeneity observed across these studies may stem from systemic iron metabolism dysregulation in PD patients[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, disruption of iron homeostasis leads to aberrant iron redistribution, driving pathological accumulation in the SNpc region, which consequently reduces peripheral blood levels of iron-related biomarkers[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].In our investigation, serum iron and ferritin levels exhibited no statistically significant differences between the PD group and the non-PD group. These biomarkers are susceptible to change by confounding variables, particularly inflammation, therefore diminishing their diagnostic reliability for assessing systemic iron homeostasis.\u003c/p\u003e\u003cp\u003eTfR is a cleaved monomer of TfR1, represents a novel and robust biomarker of iron status. Unlike other iron markers, sTfR demonstrates superior stability as it remains unaffected by inflammatory processes, providing a more reliable reflection of systemic iron metabolism [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Emerging evidence demonstrate the clinical significance of sTfR in various disease,the elevated sTfR concentrations correlate with impaired iron utilization and increased cardiovascular disease risk[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].In obese populations, higher sTfR levels predict greater susceptibility to type 2 diabetes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].TfR serves as an independent prognostic indicator for patients with diabetes-related coronary heart disease or heart failure [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].Recent investigations identified exosomal ferritin and TfR as potential PD biomarkers, suggesting their involvement in disease pathogenesis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].Our finding revealed Significant positive association between TfR levels and PD risk.The smoothing curve analysis plot indicates that as the level of TFR gradually increases, the risk of PD gradually increases.The TfR may be involved in the pathogenesis of PD.Dysregulation of iron homeostasis is often accompanied by chronic inflammation. Studies have found that TfR is positively correlated with CRP [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and antioxidant status. The elevated TfR level in multiple sclerosis patients may indicates active inflammation accompanied by persistent oxidative damage [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Iron homeostasis dysregulation and inflammatory response also exist in PD patients, which may be the reason for elevated TFR, and deserves further exploration.\u003c/p\u003e\u003cp\u003eStrength:This study is the first to explore the association between iron metabolism biomarker and PD risk based on NHANES data. The sample size is large and includes multiple races, which improves the accuracy of the study; In addition, this study compared the correlation between three iron metabolism biomarker and PD risk ,and found a positive correlation between TFR concentration and PD risk. Smoothed curve fitting analysis was employed to evaluate the dose-response relationship between TFR and PD, subgroup analysis and sensitivity analysis were performed to demonstrate the correlation\u0026rsquo;s robustness.Limitations,The reliance on self-reported anti-parkinsonian medication use as a surrogate for PD diagnosis may introduce selection bias.potentially excluding: 1)PD patients with mild symptoms who did not receive medication intervention, 2)Undiagnosed individuals in early disease stages. To overcome these issues, future research requires comprehensive evaluation by certified specialists, In addition, MDS-UPDRS Part III motor examination and Dopamine transporter (DAT) imaging can be combined to improve the accuracy of diagnosis.While this cross-sectional investigation provides preliminary evidence regarding iron dysregulation in PD, the unidimensional assessment fails to capture the temporal dynamics of iron homeostasis markers.Therefore, longitudinal follow-up and repeated measurements of these indicators will make the results more reliable.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTFR levels demonstrate a significant positive correlation with PD risk, positioning TFR as a potential biomarker for early PD prediction. Higher serum TFR concentrations may indicate elevated susceptibility to PD onset, while interventions reducing TFR levels could mitigate disease risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors\u0026apos; Contribution\u003c/p\u003e\n\u003cp\u003eXianwen Chen proposed the topic; Yan Su analyzed the structure and intellectual content of this article and made critical revisions. Sheng Cai ,Yang Xu prepared revised the manuscript. All the authors agreed to publish this article.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China[grant number 81971072].\u003c/p\u003e\n\u003cp\u003eEthics approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThis study only used publicly available data and no additional informed consent is required.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTolosa E, Garrido A, Scholz SW, Poewe W.Challenges in the diagnosis of Parkinson\u0026rsquo;s disease,Lancet Neurol. 2021, May;20(5):385-397. doi: 10.1016/S1474-4422(21)00030-2.\u003c/li\u003e\n\u003cli\u003eBloem BR, Okun MS, Klein C. 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(2005) 11:272\u0026ndash;5. doi: 10.1191/1352458505ms1171oa.\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":"serum iron, ferritin, transferrin receptor, Parkinson's disease, NHANES database, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-7672096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7672096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eParkinson's disease(PD) is a progressive neurodegenerative diorders with a rising incidence. This study aims to investigate the relationship between iron metabolism biomarkers and the risk of PD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was performed on 4,496 adults using National Health and Nutrition Examination Survey (NHANES) data (2005\u0026ndash;2010, 2015\u0026ndash;2018), including 4,496 adult participants. The logistic regression analysis was employed to detect the relationship between serum transferrin receptor(TFR) and PD. In addition, smooth curve fitting method was applied to evaluate the dose-response relationship. Sensitivity analysis, subgroup analysis, and intergroup interaction tests were conducted to evaluate the robustness of the relationship.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 4496 participants, 74 were diagnosed with PD, while 4,422 were non-PD. In both unadjusted and adjusted covariate models, multivariate logistic regression revealed a significant positive correlation between the elevated TFR and PD risk (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Smooth curve fitting analysis indicated gradual increase in PD risk with rising TFR concentrations.Subgroup analyses and interaction tests demonstrated that this association remained consistent regardless of BMI, hypertension, dyslipidemia, diabetes, smoke status, drink, and PA (all interaction P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Sensitivity analysis further supported the correlation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHigher serum TFR levels are independently correlated with increased risk of PD,suggesting a potential role in early PD prediction.Conversely, maintaining lower TFR level might reduce the risk of PD onset.These finding highlights the importance of iron metabolism in PD pathogenesis and deserve further longitudinal investigation.\u003c/p\u003e","manuscriptTitle":"Association between serum transferrin receptor and Parkinson's disease: A large cross-sectional study from NHANES","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:45:54","doi":"10.21203/rs.3.rs-7672096/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":"6a6803b2-9eb0-47f0-b0e8-b3f044876c88","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T15:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 15:45:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7672096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7672096","identity":"rs-7672096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}