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Learn more: PMC Disclaimer | PMC Copyright Notice Exp Brain Res . 2026 Apr 17;244(5):94. doi: 10.1007/s00221-026-07289-8 Search in PMC Search in PubMed View in NLM Catalog Add to search Serum albumin and uric acid: biomarkers of neurocognitive and physical function in early Parkinson’s disease Cheng-Liang Chang Cheng-Liang Chang 1 Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan City, Taiwan Find articles by Cheng-Liang Chang 1 , Tsu-Kung Lin Tsu-Kung Lin 2 Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City, Taiwan 3 Center for Parkinson’s Disease, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan 4 Center for Mitochondrial Research and Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan Find articles by Tsu-Kung Lin 2, 3, 4 , Chia-Liang Tsai Chia-Liang Tsai 1 Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan City, Taiwan Find articles by Chia-Liang Tsai 1, ✉ Author information Article notes Copyright and License information 1 Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan City, Taiwan 2 Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City, Taiwan 3 Center for Parkinson’s Disease, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan 4 Center for Mitochondrial Research and Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan ✉ Corresponding author. Received 2026 Jan 6; Accepted 2026 Mar 31; Issue date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13090273 PMID: 41995808 Abstract Serum albumin and uric acid (UA), both recognized for their antioxidant properties, have been linked to Parkinson’s disease (PD) progression. However, their associations with neurocognitive performance and physical fitness in early-stage PD remain unclear. To examine relationships between albumin and UA with cognitive performance, neurophysiological indices, and physical function in early-stage PD. Forty-eight individuals with early-stage PD were stratified into high and low groups based on median serum albumin and UA levels. Supplementary regression analyses examined dose-response associations. Neurocognitive performance was assessed via a working memory task with event-related potential (ERP) recordings. Physical fitness measures included cardiorespiratory fitness, muscle strength, and the 8-foot Timed Up-and-Go (TUG) test. Group comparisons used repeated-measures analysis of variance (ANOVA) and nonparametric tests. Higher albumin and UA levels were associated with lower motor severity; higher UA also linked to better global cognition. Both high-level groups showed faster reaction times. ERP analysis revealed higher UA associated with more negative N2 and smaller P3 amplitudes, suggesting efficient conflict monitoring and attentional processing. Both groups performed better on TUG, while only high albumin showed superior cardiorespiratory fitness. Regression models revealed a consistent linear protective trend for UA, whereas albumin exhibited an inverted U-shaped relationship with global cognition. Higher serum albumin and UA levels within the normal range are associated with superior neural efficiency and functional mobility in early-stage PD. These biomarkers may reflect systemic metabolic reserve associated with motor and cognitive function in early-stage PD. Keywords: Parkinson’s disease, Albumin, Uric acid, Neurocognitive performance, Physical fitness Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by heterogeneous motor dysfunction and cognitive decline. While dopaminergic neuron loss remains central to PD pathology, systemic metabolic disturbances, particularly oxidative stress and abnormal alpha-synuclein formation, are increasingly recognized as key contributors to disease progression (Dias et al. 2013 ; Luk 2019 ; Stefanis 2012 ). Oxidative stress involves an imbalance between reactive oxygen species (ROS) production and the body’s antioxidant defense, resulting in cellular damage. In PD, oxidative stress contributes to dopaminergic neuron degeneration and is closely linked to alpha-synuclein misfolding and aggregation (Dias et al. 2013 ; Stefanis 2012 ; Tsai et al. 2025 ). Moreover, alpha-synuclein aggregates can further increase oxidative stress by impairing mitochondrial respiration, creating a vicious cycle that amplifies neurodegeneration (Blesa et al. 2015 ). This bidirectional relationship is implicated in both motor and non-motor symptoms and is evident even in early PD (Ferrer et al. 2011 ). Against this background of systemic metabolic instability and oxidative challenge, serum albumin and uric acid (UA) stand out among the wide range of biochemical markers because they are (a) routinely measured in clinical practice, (b) have well-established physiological meaning, and (c) have been repeatedly linked to PD progression. Albumin (normal range 3.5–5.0 g/dL) is widely used as an indicator of nutritional status, liver function, vascular integrity, and systemic physiological reserve (Abedi et al. 2024 ; Levitt and Levitt 2016 ). UA, with reference ranges of 2.5–7.0 mg/dL for men and 1.5–6.0 mg/dL for women (Ren et al. 2024 ), reflects purine metabolism and renal function and is clinically relevant to metabolic balance (Copur et al. 2022 ). Beyond these general roles, both markers have been implicated in PD pathology: albumin modulates toxic alpha-synuclein aggregation (Wang et al. 2017 ; Zhang et al. 2012 ), and UA supports neuronal integrity and modulates neuroinflammatory pathways (Bakshi et al. 2020 ; Huang et al. 2017 ). Thus, these two biomarkers offer a complementary view of the body’s defense system against PD pathology, suggesting that subtle variations within these clinically normal ranges may contribute to functional heterogeneity in PD. Large-scale and longitudinal human studies consistently report that lower UA predicts more severe motor symptoms and faster cognitive decline (Moccia et al. 2015 ; Otani et al. 2023 ; Seifar et al. 2022 ; Zhai et al. 2024 ). Likewise, higher serum albumin is associated with better cognition and slower motor progression (Sun et al. 2022 ). Importantly, Wang et al. ( 2017 ) showed that both albumin and UA were significant predictors of faster decline in Mini-Mental Status Examination (MMSE) scores and Hoehn and Yahr stages, with each biomarker contributing independently to disease progression. Together, these findings provide a biologically grounded and clinically supported rationale for using albumin and UA as meaningful stratification biomarkers. However, prior work has largely relied on broad clinical measures. General cognitive screens such as the MMSE are insensitive to specific domains affected early in PD, including working memory (WM), attention, and executive function. These domains are central to the development and progression of cognitive impairment in PD (Litvan et al. 2012 ). Similarly, global motor scales such as the Hoehn and Yahr stage or UPDRS-III provide limited insight into functional components such as balance, mobility, muscular strength, and aerobic capacity. These limitations restrict our understanding of how peripheral metabolic markers, such as serum albumin and UA, relate to neurocognitive processes and physical functioning. To address this gap, the present study investigates how serum albumin and UA levels relate to specific aspects of cognitive processing and physical fitness in early-stage PD. Cognitive processing was examined using a delayed matching S1–S2 WM task paired with event-related potentials (ERPs), which offer sensitive markers of neural efficiency. This paradigm was selected for its established validity and reliability in PD research. Li et al. ( 2003 ) demonstrated that the S1–S2 task successfully distinguishes PD patients from healthy controls by capturing specific deficits in sensory gating and resource allocation that behavioral measures alone often miss. Furthermore, our recent work confirmed that this task provides distinct neurophysiological parameters capable of detecting sensitive between-group differences in neural plasticity (Chang et al. 2024 ). ERP components such as N2, associated with conflict monitoring, and P3, linked to attentional allocation and memory updating, are well suited for detecting subtle cognitive alterations in PD (Seer et al. 2016 ; Xu et al. 2022 ). Physical fitness was assessed as a multidimensional construct including muscular strength, aerobic capacity, body composition, and functional mobility. These fitness components have been independently associated with cognitive decline and functional deterioration in PD and aging populations (Barichella et al. 2024 ; Morris et al. 2019 ), underscoring their relevance to disease progression. Notably, accumulating evidence suggests that albumin and UA may be relevant to these functional domains. Lower serum albumin has been associated with accelerated muscle loss in aging populations (Snyder et al. 2012 ; Visser et al. 2005 ), and higher UA with greater muscular strength (Oncel Yoruk et al. 2023 ; Wu et al. 2013 ). However, despite this evidence in aging populations, few studies have directly examined the associations between serum albumin, UA, and physical fitness in PD. This gap warrants further exploration, as it may offer insights into disease variability and new intervention targets. Taken together, important gaps remain in understanding how serum albumin and UA relate to domain-specific cognitive processes and multidimensional physical fitness in PD. Because the goal of the present study is to characterize variability within early-stage PD rather than to contrast PD with healthy aging, our analytic focus is on differences associated with biomarker levels inside the clinically normal range. This approach is well aligned with prognostic and biomarker research, where internal stratification is commonly used to examine whether naturally occurring biochemical variation corresponds to meaningful differences in cognitive processing or functional capacity. By minimizing confounds unrelated to PD pathology, this design enables clearer interpretation of biomarker–function relationships that may contribute to disease heterogeneity. Accordingly, we hypothesized that higher serum albumin and UA levels would be associated with better neurocognitive performance in the WM task and superior physical fitness. Method Participants Participants were recruited through neurologist referrals from the Parkinson’s Disease Center of Kaohsiung Chang Gung Memorial Hospital and National Cheng Kung University Hospital. The sample comprised 48 patients with idiopathic PD (25 male, 23 female). Inclusion criteria were: (a) a confirmed PD diagnosis by hospital neurologists with medically stable status; (b) a modified Hoehn & Yahr score of 1–2, indicating mild to moderate PD; (c) absence of major comorbidities such as dementia or depression; (d) no structural brain abnormalities (e.g., stroke or malignancy) on MRI; and (e) age between 50 and 80 years. Exclusion criteria included: (a) any neurological condition other than PD; (b) dementia, defined as a MoCA score < 24; (c) abnormal serum albumin or UA levels (i.e., outside normal range) (Das et al. 2014 ; Gotsman et al. 2019 ); and (d) medications known to affect cognition or the targeted biomarkers. Written informed consent was obtained from all participants. The study was approved by the Institutional Review Board of Kaohsiung Chang Gung Memorial Hospital. Experimental procedure Participants attended a single laboratory session for clinical and neurocognitive assessments. To reduce confounding effects from medication or circadian rhythms, all measurements were conducted during the “on” medication phase between 09:00 and 12:00. Participants were asked to refrain from strenuous exercise and consuming stimulants (e.g., coffee, alcohol, tobacco) for 24 h prior. Upon arrival, participants received a briefing on the procedures and provided informed consent. They then completed demographic and medical history forms, the MoCA, the Beck Depression Inventory-II (BDI-II), and the seven-day physical activity recall (7-day PAR) questionnaire. These assessments were included to control for potential confounders affecting neurocognitive outcomes. Following the questionnaires, the Unified PD Rating Scale (UPDRS) was administered to assess both motor and non-motor symptom severity. A certified physical therapist conducted fitness evaluations, including body composition, cardiopulmonary function (six-minute step test; VO₂max calculated using a previously published formula), muscle strength (handgrip, biceps curl, and sit-to-stand tests), and balance/coordination (8-foot Timed Up-and-Go test). Subsequently, participants were guided to an acoustically shielded EEG laboratory, maintained at 23–25 °C under controlled lighting. After being seated comfortably in front of a computer, an EEG cap and electro-oculographic electrodes were applied using standard reference points. Participants then performed a cognitive task while EEG data were recorded to assess ERPs. Cognitive task The delayed matching S1–S2 paradigm and ERP procedures were identical to those used in our previously published randomized controlled trials (Chang et al. 2024 ; Tsai and Lin 2023 ). Each participant completed 270 trials (135 per block), including 180 incongruent and 90 congruent conditions. Participants were instructed to compare S1 and S2 as quickly and accurately as possible. They pressed the “M” key with the right index finger if S1 and S2 matched (congruent) and the “N” key with the left index finger if they differed (incongruent). A 3-minute rest was provided between blocks to reduce fatigue. A practice session preceded the task, which continued until participants achieved an acceptable accuracy rate (error rate ≤ 5%). Neuropsychological outcomes (accuracy and reaction time) and ERP components (N2 and P3) were recorded throughout the task. ERPs recording and data processing EEG signals were recorded using a 32-channel electrode cap (Quik-Cap, Compumedics Neuroscan, El Paso, TX, USA) following the International 10–20 system, with a 500 Hz sampling rate per channel. Electrode impedance was maintained below 5 kΩ. Signals were referenced to linked mastoids, and the ground electrode was placed at the mid-forehead. Vertical and horizontal eye movements were monitored via electro-oculographic recordings using adhesive electrodes placed above and below the left eye and at the outer canthus of the right eye. Data were acquired using a SynAmps amplifier with a 60 Hz notch filter and stored for offline processing with SCAN 4.5 software (Compumedics Neuroscan). A 0.1–50 Hz bandpass filter was applied to raw EEG data. Neuropsychological performance (ARs and RTs) was processed using Neuroscan Stim2 software. Trials with errors, omissions, anticipatory responses (RT < 200 ms), or excessively delayed responses (RT > 2 SDs above the mean) were excluded to avoid skewing group-level statistics. For ERP analysis, EEG data were segmented into epochs from 100 to 1,000 ms relative to stimulus onset. Ocular artifacts were corrected using a standard reduction algorithm, and epochs exceeding ± 100 µV were rejected. The N2 and P3 components were analyzed at Fz, Cz, and Pz electrodes. N2 was defined within 180–350 ms, and P3 within 300–800 ms post-stimulus. Blood sampling and analysis A certified phlebotomist collected a 10 mL fasting venous blood sample from each participant’s antecubital vein between 09:00 and 12:00. Serum was separated via centrifugation and processed for biochemical analysis. Serum albumin was measured using the bromocresol green (BCG) method, and serum UA was measured using an enzymatic colorimetric assay. Analyses were conducted with the Beckman Coulter ® AU680 automated system. All measurements were performed by certified laboratory technologists following standardized quality control protocols to ensure accuracy and reliability. Statistical analysis Serum albumin and UA levels are known to correlate with clinical performance, though these associations may follow nonlinear trends (Sun et al. 2022 ). To address this, participants were divided into high and low groups based on median biomarker values, following approaches used in prior studies (Tang et al. 2024 ). Participants above the median were classified as high, and those below as low. Demographic and fitness differences between groups were assessed using the Mann–Whitney U test, a nonparametric method suitable for small or non-normally distributed samples. Sex distribution was evaluated with Pearson’s chi-square test. Behavioral data (RT and AR) were analyzed using a 2 ( group:low and high ) × 2 ( condition:congruent and incongruent ) mixed-model repeated-measures analysis of variance (ANOVA). ERP data (N2 and P3 amplitudes and latencies) were analyzed using a 2 ( group:low and high ) × 2 ( condition:congruent and incongruent ) × 3 ( electrode: FZ,CZ and PZ ) mixed-model repeated-measures ANOVA. For all ANOVAs, Bonferroni-corrected post hoc comparisons were conducted when significant main effects or interactions emerged. Greenhouse–Geisser corrections were applied when Mauchly’s test indicated sphericity violations. Data normality and variance homogeneity were tested using the Kolmogorov–Smirnov and Levene’s tests, respectively. Effect sizes were reported using partial eta squared (η²p), interpreted as small (0.01–0.059), medium (0.06–0.139), or large (≥ 0.14). The alpha level was set at p < .05. To further validate the robustness of our findings, we performed a secondary sensitivity analysis using hierarchical linear regression models, treating serum albumin and UA as continuous predictors (Thabane et al. 2013 ). This analysis focused specifically on the primary clinical outcomes (i.e., UPDRS-III and MoCA). For these core measures, we explicitly tested for both linear and non-linear (quadratic) relationships to evaluate whether metabolic levels exhibit threshold or saturation effects, as suggested by recent literature (Sun et al. 2022 ). All models were adjusted for potential confounders, including age, sex, BMI, and disease duration. While continuous regression is generally preferred for maximizing statistical power, it is susceptible to Type II errors when the predictor variable has a restricted range (Sackett and Yang 2000 ). Given that our cohort consisted of early-stage PD patients with biomarker levels largely confined to the normal physiological range (resulting in low variance), we retained the median-split stratification as the primary method for phenotypic characterization and visualization. This complementary approach allows for the detection of distinct clinical profiles that might be attenuated in linear models due to the narrow range inherent in a clinically stable cohort. Results Demographic characteristics and biomarker distribution Table 1 summarizes cohort characteristics and group comparisons based on serum albumin and UA levels. Forty-eight individuals with early-stage PD (Hoehn and Yahr stages I–II) were included. Participants were divided into high and low groups using median values: 4.4 g/dL for albumin and 4.75 mg/dL for UA. Although all values were within normal clinical ranges, this stratification created groups with significantly different biomarker levels. In the albumin model, the high group had higher albumin levels than the low group (M = 4.63 vs. 4.18, p < .001), with no significant UA differences. Likewise, in the UA model, the high group had significantly higher UA levels than the low group (M = 5.83 vs. 3.65, p < .001), with no significant differences in albumin levels. These results confirm successful and independent group stratification. Table 1. Demographic characteristics across the biomarker groups Overall Serum albumin (g/dL) (median = 4.4) p value Uric acid(mg/dL) (medium = 4.75) p value Low (3.5–4.4) High (4.5–5.0) Low (2.5–4.6) High (4.9–7.0) N 48 25 23 - 24 24 - Serum albumin (g/dL) 4.39 (0.31) 4.18 (0.25) 4.63 (0.16) < 0.001* 4.64 (1.20) 4.85 (1.35) 0.718 Uric acid (mg/dL) 4.74 (1.27) 4.37 (0.28) 4.45 (0.38) 0.917 3.65 (0.66) 5.82 (0.68) < 0.001* Age 64.56 (7.18) 66.2 (6.52) 62.78 (7.44) 0.096 64.29 (7.20) 64.83 (7.16) 0.918 Sex (M/F) 25/23 15/10 10/13 0.252 14/10 9/15 0.149 Education (year) 12.44 (1.96) 12.20 (2.12) 12.69 (1.84) 0.394 12.58 (2.20) 12.29 (1.78) 0.617 Illness duration (year) 6.27 (6.22) 6.12 (5.55) 6.43 (6.86) 0.893 5.71 (5.32) 6.83 (6.95) 0.604 MoCA 26.96 (2.29) 26.72 (2.20) 27.22 (2.38) 0.450 26.13 (2.32) 27.79 (1.96) 0.015* UPDRS-III 6.65 (4.96) 7.84 (5.07) 5.35 (4.49) 0.038* 8.75 (5.87) 4.54 (2.43) 0.026* BDI-II 7.38 (6.86) 8.16 (6.95) 6.52 (6.66) 0.315 8.17 (5.97) 6.58 (7.57) 0.181 Social participation 8.75 (1.85) 8.64 (1.72) 8.87 (1.98) 0.490 8.29 (1.57) 9.21 (1.99) 0.125 7-day PAR (Kcal/day) 1,264.37 (909.37) 1,235.54 (991.96) 1,295.7 (808.95) 0.620 1,254.36 (1,110.69) 1,274.37 (648.14) 0.244 Open in a new tab MoCA: Montreal Cognitive Assessment; UPDRS-III: Unified Parkinson’s disease rating scale- motor subscale; BDI-II: Beck Depression Inventory—Second Edition; 7-day PAR: seven-day physical activity recall questionnaire . Values are presented as mean (SD) . p values indicate between-group differences based on the Mann–Whitney U test. Bold with an asterisk (*) indicates statistical significance at p < .05 No significant group differences were found in age, sex, illness duration, depression scores, social interaction, or physical activity. However, participants with higher albumin levels had lower UPDRS-III scores (M = 5.35 vs. 7.84, p = .038). Similarly, the high UA group had significantly higher MoCA scores (M = 27.79 vs. 26.13, p = .015) and lower UPDRS-III scores (M = 4.54 vs. 8.75, p = .026) compared to the low UA group. Neuropsychological indices Overall neuropsychological indices Across all participants, responses were faster in the congruent than in the incongruent condition (M = 558.93 vs. 618.71 ms, F[1,46] = 13.10, p = .001, η²p = 0.222). Interestingly, accuracy was higher in the incongruent condition (M = 92.39%) than in the congruent condition (M = 89.79%, F[1,46] = 5.46, p = .024, η²p = 0.106). These task-related effects were consistent across biomarker groups and are not reiterated unless significant group interactions occurred. Serum albumin and neuropsychological indices As shown in Fig. 1 , a significant condition × group interaction was found for RT (F[1,46] = 10.74, p = .002, η²p = 0.189). Post hoc tests revealed that the high albumin group responded faster than the low albumin group in the incongruent condition (M = 557.49 vs. 675.11 ms, p = .019). Additionally, within the low albumin group, RTs were significantly faster in the congruent than in the incongruent condition (M = 566.47 vs. 675.11 ms, p < .001). No significant group or interaction effects were observed for accuracy. Fig. 1. Open in a new tab Reaction times (mean ± standard deviation) during the delayed matching of the S1–S2 paradigm for each group and condition Uric acid and neuropsychological indices Figure 1 indicated a significant condition × group interaction for RT (F[1,46] = 4.28, p = .044, η²p = 0.085). Post hoc analysis showed that the high UA group had faster responses than the low UA group in the incongruent condition (M = 560.43 vs. 654.37 ms, p < .001). No significant differences were found for accuracy. Neurophysiological indices Overall neurophysiological indices As presented in Table 2 , ERP analysis showed significant effects of task condition and electrode site. For N2 amplitude, there was a main effect of electrode (F[2,92] = 29.03, p < .001, η²p = 0.387), with the most negative amplitude at Pz (M = 0.68 µV), followed by Cz (M = 2.35 µV) and Fz (M = 2.92 µV). Table 2. Summary of repeated-measures ANOVA results for neurophysiological indices N2 amplitude N2 latency P3 amplitude P3 latency Overall Condition ns p = .004, η²p = 0.168 ns ns Electrode p < .001, η²p = 0.387 p < .001, η²p = 0.189 ns ns Condition x Electrode ns p = .007, η²p = 0.103 ns ns Serum albumin Group ns ns ns ns Group x Condition ns ns ns ns Group x Electrode ns p < .001, η²p =0.249 ns ns Group x Condition x Electrode ns ns ns ns Uric acid Group p = .001, η²p = 0.205 ns p = .034, η²p = 0.094 ns Group x condition p = .035, η²p = 0.093 ns p = .014, η²p = 0.125 ns Group x Electrode p = .034, η²p = 0.073 ns ns ns Group x Condition x Electrode ns ns ns ns Open in a new tab For N2 latency, main effects of condition and electrode were found. Latencies were shorter in the incongruent condition than in the congruent condition (M = 204.81 vs. 221.07 ms; F[1,46] = 9.31, p = .004, η²p = 0.168). Electrode differences were also significant (F[2,92] = 10.71, p < .001, η²p = 0.189), with the shortest latency at Pz (M = 201.69 ms), followed by Fz (M = 216.38 ms) and Cz (M = 220.76 ms). A significant condition × electrode interaction was also observed (F[2,92] = 5.28, p = .007, η²p = 0.103). Post hoc tests indicated the largest condition effects at Fz and Cz, and that congruent condition responses mainly drove electrode-related differences. No significant main or interaction effects were found for the P3 component across participants. Serum albumin and neurophysiological indices Figure 2 presents the grand average ERP waveforms by serum albumin group. RM-ANOVA for N2 latency revealed a significant electrode × group interaction (F[2,92] = 15.284, p < .001, η²p = 0.249). Post hoc analysis showed that N2 latency was shortest in the high albumin group at Pz (M = 196.83 ms), followed by Fz (M = 231.07 ms) and Cz (M = 233.72 ms). No electrode-related differences were observed within the low albumin group. No other significant effects or interactions related to albumin were found for N2 amplitude or the P3 component. Fig. 2. Open in a new tab Grand average ERP waveforms for congruent and incongruent conditions during the delayed matching S1–S2 paradigm for Albumin groups Uric acid and neurophysiological indices Figure 3 displays the grand average ERP waveforms by UA group. RM-ANOVA for N2 amplitude revealed a significant main effect of group (F[1,46] = 11.845, p = .001, η²p = 0.205), with the high UA group showing more negative amplitudes (M = 1.26 µV) than the low UA group (M = 2.71 µV). A significant condition × group interaction (F[1,46] = 4.739, p = .035, η²p = 0.093) indicated that this difference occurred only in the congruent condition. Additionally, an electrode × group interaction (F[2,92] = 3.648, p = .034, η²p = 0.073) revealed that group differences were significant at Fz and Cz but not at Pz. Fig. 3. Open in a new tab Grand average ERP waveforms for congruent and incongruent conditions during the delayed matching S1–S2 paradigm for Uric acid groups For P3 amplitude, a significant main effect of group was observed (F[1,46] = 4.800, p = .034, η²p = 0.094), with the low UA group showing higher amplitude than the high UA group (M = 13.23 vs. 9.67 µV). A significant condition × group interaction (F[1,46] = 6.558, p = .014, η²p = 0.125) indicated that group differences were only evident in the congruent condition (M = 14.09 µV vs. 9.22 µV, p = .007). No other significant main effects or interactions involving UA were found for N2 or P3 latencies. Physical fitness and biomarker groups As shown in Table 3 , participants with higher serum albumin levels demonstrated superior performance in select physical fitness measures. Estimated cardiorespiratory capacity was significantly higher in the high albumin group (M = 30.83) compared to the low group (M = 24.55, p = .018). Additionally, the high albumin group showed faster performance on the 8-foot Timed Up-and-Go (TUG) test (M = 7.04 vs. 8.96, p = .023). No significant differences were found between groups in BMI, handgrip strength, sit-to-stand, or biceps curl tests. Table 3. Physical fitness scores across the biomarker groups Overall Serum albumin (median = 4.4) p value Uric acid (medium = 4.75) p value Low (3.5–4.4) High (4.5–5.0) Low (2.5–4.6) High (4.9–7.0) N 48 25 23 - 24 24 - BMI (kg/m²) 23.76 (3.33) 23.98 (3.51) 23.52 (3.10) 0.642 22.29 (3.14) 25.23 (2.82) 0.005* VO2max (mL/kg/min) 27.56 (8.59) 24.55 (7.16) 30.83 (8.82) 0.018* 28.61 (9.68) 26.51 (7.18) 0.606 Handgrip (kg) 28.79 (8.61) 29.37 (8.26) 27.09 (8.83) 0.403 27.1 (9.75) 29.46 (7.74) 0.252 30s Biceps curl (reps) 29.23 (8.94) 29.16 (8.61) 29.30 (9.31) 0.959 28.79 (9.75) 29.67 (8.05) 0.584 30 s sitting to stand (reps) 15.08 (5.54) 14.96 (6.00) 15.22 (4.99) 0.487 14.29 (4.49) 15.88 (6.33) 0.450 8-ft Timed Up-and-Go (s) 8.04 (2.89) 8.96 (3.57) 7.04 (1.26) 0.023* 8.89 (3.45) 7.19 (1.82) 0.029* Open in a new tab Values are presented as mean (SD). p values indicate between-group differences based on the Mann–Whitney U test. Bold with an asterisk (*) indicates statistical significance at p < .05 The high UA group exhibited a higher BMI than the low UA group (M = 25.23 vs. 22.29, p = .005) and outperformed the low group on the TUG test (M = 7.19 vs. 8.89, p = .029). No significant differences were found in cardiorespiratory capacity, muscular strength, or other fitness measures between the UA groups. Hierarchical regression analysis of biomarkers and clinical outcomes To further validate the associations observed in the group comparisons and explore potential non-linear dose-response relationships, hierarchical regression analyses were performed (Table 4 ). The adjusted dose-response trends for the MoCA and UPDRS-III scores are visualized in Fig. 4 . Table 4. Hierarchical regression analysis of serum Albumin and uric acid on clinical outcomes Model R 2 ΔR 2 β p Serum Albumin UPDRS-III Controls 0.039 0.039 - - Linear 0.083 0.044 -0.383 0.130 Quadratic 0.134 0.051 -1.266 0.100 MoCA Controls 0.641 0.641 - - Linear 0.832 0.192 0.796 < 0.001* Quadratic 0.874 0.042 -1.148 < 0.001* Uric acid UPDRS-III Controls 0.039 0.039 - - Linear 0.208 0.169 -0.432 0.004* Quadratic 0.208 0.001 -0.203 0.847 MoCA Controls 0.054 0.054 - - Linear 0.165 0.111 2.393 0.021* Quadratic 0.167 0.002 -0.296 0.768 Open in a new tab Regression analyses were performed specifically for primary clinical outcomes (MoCA, UPDRS-III). Models adjusted for age, gender, BMI, and disease duration. UPDRS-III: Unified Parkinson’s disease rating scale-motor subscale; MoCA: Montreal Cognitive Assessment. Bold with an asterisk (*) indicates statistical significance at p < .05 Fig. 4. Open in a new tab Adjusted dose-response associations of serum uric acid and albumin with motor severity and cognitive function For UA, linear regression models revealed significant protective associations across motor and cognitive domains. After adjusting for covariates (age, sex, BMI, and disease duration), higher UA levels significantly predicted lower motor severity (UPDRS-III: = -0.432, p = .004) and better global cognition (MoCA: = 0.351, p = .021). The addition of a quadratic term did not significantly improve model fit for any outcome, confirming a linear relationship within the physiological range. For serum albumin, the analysis revealed distinct patterns. Most notably, a highly significant non-linear relationship was observed for the MoCA score. While the linear model was significant ( R 2 = 0.192, p < .001), the addition of the quadratic term further improved the model fit ( R 2 = 0.042, p < .001). The quadratic coefficient was negative ( = -1.148), indicating an inverted U-shaped association with an estimated inflection point at approximately 4.74 g/dL (Cohen et al. 2013 ). This suggests that global cognitive performance peaks at this optimal albumin level before plateauing. Regarding motor symptom severity, continuous regression models for UPDRS-III did not reach statistical significance, potentially attributable to the restricted range of albumin values in this early-stage cohort compared to the binary stratification approach. Discussion Main findings Building on earlier studies linking low serum albumin and UA to faster PD progression, our findings confirmed these associations and extended current understanding by integrating detailed neurocognitive and physical fitness assessments. Higher serum albumin and UA levels were associated with more favorable clinical outcomes in individuals with early-stage PD. Specifically, both biomarkers were linked to reduced motor symptom severity (UPDRS-III), while higher UA was also associated with better global cognitive performance (MoCA). Hierarchical regression analyses further clarified the nature of these associations with UA exhibited a consistent linear protective effect across motor, cognitive, and functional domains, whereas serum albumin showed an inverted U-shaped relationship with global cognition, suggesting an optimal therapeutic window. Additionally, participants with higher biomarker levels showed faster reaction times in the incongruent WM task condition. ERP analyses further revealed that higher UA levels were associated with more negative N2 amplitudes and smaller P3 amplitudes, indicating potential cognitive processing benefits. Regarding physical fitness, both high albumin and UA groups performed better on the 8-foot TUG test, while superior cardiorespiratory fitness was observed only in the high albumin group. Crucially, within the clinical normal range, elevated levels of these biomarkers may not reflect direct central neuroprotection but rather a more favorable systemic physiological state. The observed functional advantages are consistent with the neurobiological framework outlined in the Introduction, in which oxidative stress plays a central role in PD pathophysiology. As endogenous antioxidants, albumin and UA contribute to counteracting oxidative stress and reactive oxygen species–mediated damage. Higher concentrations within the physiological range may therefore reflect a greater systemic capacity to withstand oxidative stress, serving as markers of metabolic reserve rather than direct therapeutic agents. This preserved physiological status is associated with better functional maintenance and may contribute to resilience against disease-related decline. The specific roles of albumin and UA are discussed separately below. Serum albumin and clinical performance Although the initial median-split comparison did not reveal significant group differences in MoCA score, a key finding from our regression was the inverted U-shaped association between serum albumin and global cognition, with an estimated inflection point at approximately 4.74 g/dL. The quadratic regression model unveiled this non-linear pattern, which was likely masked by the binary classification. This finding offers a nuanced perspective compared to Sun et al. ( 2022 ), who reported a linear threshold effect below 3.9 mg/dL. Our data suggest that while higher albumin levels are generally beneficial due to antioxidant properties and amyloid-beta binding capacity (Prajapati et al. 2011 ), benefits may plateau or even diminish as levels approach the upper physiological limit. The identified peak at estimated 4.74 g/dL aligns with the upper range of normal clinical values, implying that maintaining albumin within a “high-normal” range yields maximal cognitive benefit, whereas supraphysiological levels might reflect hemoconcentration or other metabolic imbalances that offset neuroprotection. Despite the complex relationship with global cognition, the high albumin group exhibited faster reaction times on the WM task. The absence of a speed–accuracy trade-off suggests enhanced cognitive efficiency rather than superficial gains in speed (Bo et al. 2011 ). These findings support previous research highlighting albumin’s neuroprotective effects, including reducing oxidative stress and inhibiting alpha-synuclein aggregation, mechanisms that may preserve function in memory-related regions. ERP analysis revealed a significant group-by-electrode interaction for N2 latency, with the high albumin group showing significantly shorter latency at Pz than at Fz or Cz. No such pattern was observed in the low albumin group. This finding may partially explain their faster behavioral performance. The delayed matching S1–S2 task requires participants to encode, retain, and compare visual stimuli, placing considerable demand on visual discrimination processes rather than simple motor inhibition. Prior studies suggest that posterior regions, particularly the parietal scalp, are integral to visual comparison and attentional allocation (Folstein and Van Petten 2008 ). Thus, faster N2 latency at Pz may reflect enhanced posterior attentional function in the high albumin group. However, this posterior advantage did not extend to significant group differences in N2 or P3 amplitudes or P3 latency, leaving the full neurophysiological mechanisms unclear. One explanation may lie in the narrow albumin range within the sample, with most participants falling within clinically normal limits (Walker et al. 1990 ). This reduced variability could weaken contrasts between groups and mask potential effects. Furthermore, serum albumin may not show substantial decline in early PD, making related differences harder to detect in this stage. More pronounced effects may emerge in advanced PD or in samples with wider albumin distributions. The possibility of a Type II error due to moderate sample size also warrants consideration. Notably, the high albumin group showed significantly faster performance on the 8-foot TUG test, indicating better balance and mobility. This finding aligns with prior research linking albumin to physical function and frailty in older adults (Aung et al. 2011 ; Snyder et al. 2012 ; Visser et al. 2005 ) and suggests a role for albumin in supporting motor efficiency and functional independence in PD. The TUG test involves multiple motor tasks, standing up, walking, turning, and sitting, that are often impaired in PD (Morris et al. 2001 ; Nocera et al. 2013 ). Therefore, superior TUG performance may help explain the lower UPDRS-III scores in the high albumin group, supporting the notion that serum albumin contributes to preserving functional mobility and mitigating motor symptoms in early-stage PD. Given prior evidence linking albumin with aerobic capacity, the higher estimated VO₂max observed in the high albumin group may further illuminate differences in neurocognitive performance. Aerobic fitness has been associated with enhanced neuroplasticity, synaptic integrity, and functional brain connectivity (Duchesne et al. 2015 ; Petzinger et al. 2013 ; Schootemeijer et al. 2020 ). To verify this relationship, we conducted a correlation analysis between VO₂max and reaction time in the incongruent condition. A significant negative correlation emerged ( r = − .308, p = .033), indicating that individuals with higher aerobic capacity responded more quickly during the WM task. This supports the proposed link between aerobic fitness and cognitive efficiency in PD, suggesting that better cardiorespiratory health may help preserve neurocognitive performance through neuroprotective mechanisms. Future research with larger samples and multimodal neuroimaging could clarify the neural pathways through which albumin exerts its protective effects. Uric acid and clinical performance Similar to the albumin findings, no significant demographic differences were observed between high and low UA groups. Participants with higher UA levels showed better global cognitive performance (MoCA) and reduced motor severity (UPDRS-III). Regression analyses further confirmed these relationships with no evidence of a threshold or saturation effect (quadratic terms were non-significant). This suggests that within the physiological range observed in our cohort, higher UA levels consistently confer neuroprotective benefits without reaching a “tipping point” of diminishing returns, aligning with previous research linking UA concentrations to slower PD progression and reduced cognitive decline (Annanmaki et al. 2011 ; van Wamelen et al. 2020 ). These results further support the role of UA as a potent endogenous antioxidant with neuroprotective properties in PD (Bakshi et al. 2020 ). Beyond global cognition, individuals in the high UA group responded more rapidly during the WM task, suggesting greater cognitive efficiency. However, a notable pattern emerged regarding accuracy: participants demonstrated higher accuracy for incongruent trials compared to congruent trials. This result, while seemingly paradoxical, likely reflects the specific energetic and temporal demands of the task structure. Incongruent trials constituted 67% of the total, creating a high-probability environment where the “difficult” condition became the expected default. As noted by Steinborn and Langner ( 2012 ), temporal preparation and arousal are modulated by probability; the cognitive system optimizes activation for the most frequent event. Consequently, participants likely maintained a sustained state of high control (a “proactive” strategy) to handle the frequent incongruent stimuli. When a congruent (rare) trial appeared, it violated this preparatory set, resulting in a “surprise” cost that lowered accuracy. This interpretation aligns with the energetic-cognitive control theory discussed by Schumann et al. ( 2022 ), which suggests that maintaining attention requires the regulation of energetic resources; the high UA group’s superior speed may reflect a more efficient upregulation of these resources to meet the task’s skewed demands. ERP analyses offered further insight into this efficiency. Higher UA levels were associated with more negative N2 amplitudes at Fz and Cz (frontocentral sites) during congruent trials. Given the probability bias described above, the rare congruent trials required a rapid shift from the default “incongruent-preparation” mode. The enhanced frontocentral N2 in the high UA group suggests superior conflict monitoring and reactive control—essentially, a better ability to detect and adjust to the “unexpected” easy condition. Furthermore, ERP data revealed a distinct topographic pattern. Group differences in N2 amplitude emerged at Fz and Cz, but not at Pz, suggesting a frontocentral enhancement of conflict monitoring. This pattern implicates the anterior cingulate cortex and prefrontal networks, regions central to adjusting cognitive control in the presence of conflicting habitual responses (Folstein and Van Petten 2008 ). Prior evidence suggests that UA may enhance such functions through several pathways. Animal studies have shown that UA upregulates mitochondrial fusion markers (e.g., MFN1/2, OPA1), promoting bioenergetic optimization and reducing oxidative stress (Lee et al. 2022 ). Improved mitochondrial dynamics may preserve ATP availability for frontocortical activity, consistent with our findings. However, as mitochondrial measures were not collected in this study, this mechanism remains speculative and requires further validation. Additionally, the smaller P3 amplitudes observed in the high UA group during congruent trials may reflect more efficient attentional resource allocation. This is supported by the observation that these individuals responded more quickly without sacrificing accuracy, suggesting that reduced neural activation coincided with superior behavioral outcomes. This pattern aligns with earlier research indicating that, when performance is maintained or enhanced, smaller P3 amplitudes may reflect neural efficiency rather than impairment (Polich 2007 ). Notably, individuals with higher UA levels demonstrated faster performance on the 8-foot TUG test, reflecting better balance and mobility. This aligns with UA’s proposed role in supporting neuromuscular function through its antioxidant capacity (Oncel Yoruk et al. 2023 ). However, unlike the albumin groups, no significant differences in cardiorespiratory fitness were found between the high and low UA groups, suggesting that aerobic capacity may not mediate UA’s cognitive benefits in this cohort. Additionally, the high UA group had significantly higher BMI than the low UA group. While elevated BMI can reflect greater muscle mass, it may also indicate increased fat mass and early signs of overweight or obesity. This raises an important clinical concern. Although UA may confer neuroprotective and functional advantages, elevated levels are also associated with metabolic risks, including gout, cardiovascular disease, and obesity-related complications (Du et al. 2024 ; Mao et al. 2024 ). Thus, higher UA levels are not universally beneficial. Balanced nutrition and body composition management remain essential to optimize UA’s benefits while mitigating associated risks. Future research should further examine the complex relationship between UA, body composition, and metabolic health in individuals with PD. Strength and limitations This study has several notable strengths. It is among the first to comprehensively investigate the roles of serum albumin and UA in relation to neurocognitive and physical fitness outcomes in early-stage PD, using detailed behavioral, electrophysiological, and physical assessments. The inclusion of ERPs offers valuable neurophysiological insight beyond conventional cognitive testing, enhancing understanding of cognitive processing efficiency. Furthermore, our statistical strategy adopted a complementary approach: while median-split stratification highlighted clinically meaningful differences between high and low biomarker profiles, hierarchical regression analyses further refined these findings, identifying a linear protective role for UA and a novel inverted U-shaped non-linear association for albumin in cognition. Nonetheless, several limitations should be acknowledged. First, detailed information regarding dopaminergic medication dosage (e.g., Levodopa Equivalent Daily Dose, LEDD) was not available for analysis. Although our cohort consisted primarily of early-stage patients (Hoehn and Yahr stages 1–2) who typically maintain lower and stable medication regimens, we cannot fully rule out the potential confounding effect of symptomatic treatment on motor and functional assessments. Similarly, unmeasured systemic confounders such as hydration status, subclinical anemia, and inflammatory markers may have influenced biomarker levels and cannot be fully excluded as alternative explanations for the observed associations. Future studies should incorporate both LEDD and these variables as covariates to more rigorously isolate the independent contributions of metabolic biomarkers to functional outcomes in PD. Second, interpretations of our results are constrained by the relatively small sample size, as the restricted range of biomarker values in this early-stage cohort may have reduced the sensitivity to detect more pronounced effects. Third, the cross-sectional design restricts causal interpretations. While we observed significant associations, we cannot determine whether optimizing biomarker levels directly leads to improvements in motor or cognitive function, or if these levels simply reflect better overall health status. Long-term longitudinal tracking is required to validate the predictive value of these biomarkers on disease progression. Furthermore, the absence of a healthy age-matched control group limits our ability to determine whether the observed associations are specific to PD pathophysiology or reflect normative aging processes. Fourth, we assessed peripheral serum concentrations of albumin and UA, but their central nervous system activity remains uncertain due to the blood–brain barrier. Direct brain-level assessment (e.g., CSF sampling) was not feasible in this study. Future studies with longitudinal designs, larger and more diverse samples, and multimodal assessments are needed to clarify the mechanisms and clinical significance of albumin and UA in PD. Conclusion This study demonstrates that in early-stage PD, higher levels of serum albumin and UA, even within the clinically normal range, are associated with superior motor function, cognitive processing speed, and neural efficiency. These exploratory findings do not necessarily imply a direct neuroprotective drug-like effect of these molecules. Instead, they generate the hypothesis that these biomarkers may serve as indicators of systemic physiological reserve and metabolic health. Patients with metabolic levels within an optimal high-normal window are associated with better maintained functional capacity and neural efficiency despite disease pathology. However, given the metabolic risks associated with high UA (e.g., gout, cardiovascular disease) and the potential for hemoconcentration or metabolic strain linked to supraphysiological albumin, large-scale prospective studies are needed. Nevertheless, clinical management should focus on maintaining these biomarkers at optimal, balanced levels rather than controlling them at lower levels or maximizing them indiscriminately. Acknowledgements All authors express gratitude to the participants for their valuable time and willingness to participate in this study. This study was supported by a grant from the Ministry of Science and Technology, Taiwan (MOST 108-2410-H-006-097-MY3). We express our gratitude to the participants for their time and willingness to participate in this study. Chang C.L.: Writing – Original draft, Formal analysis, Data curation, Conceptualization. Tsai C.L.: Funding acquisition, Project administration, Validation, Supervision. Tsai C.L. & Lin T.K: Writing – Review & editing manuscript, supervision. Author contributions Chang C.L.: Writing – Original draft, Formal analysis, Data curation, Conceptualization. Tsai C.L.: Funding acquisition, Project administration, Validation, Supervision. Tsai C.L. & Lin T.K: Writing – Review & editing manuscript, supervision. Funding This study was supported by a grant from the Ministry of Science and Technology, Taiwan (MOST 108-2410-H-006-097-MY3). 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